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Gender Differences in Lung Cancer Treatment - Scholar Commons

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University of South Florida

Scholar Commons Graduate Theses and Dissertations

Graduate School

2011

Gender Differences in Lung Cancer Treatment and Survival Margaret Anne Kowski University of South Florida, [email protected]

Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the American Studies Commons, Biostatistics Commons, and the Epidemiology Commons Scholar Commons Citation Kowski, Margaret Anne, "Gender Differences in Lung Cancer Treatment and Survival" (2011). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/3191

This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]

Gender Differences in Lung Cancer Treatment and Survival

by

Margaret Anne Kowski

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Epidemiology and Biostatistics College of Public Health University of South Florida

Co-Major Professor: Thomas J. Mason, Ph.D. Co-Major Professor: Heather G. Stockwell, Sc.D. Getachew Dagne, Ph.D. Tatyana Zhukov, Ph.D.

Date of Approval: April 11, 2011

Keywords: Gender Specific Survival, Lung Malignancy, Chemotherapy, Radiation Therapy, Surgery Copyright © 2011, Margaret Anne Kowski

TABLE OF CONTENTS LIST OF TABLES ............................................................................................................ vii LIST OF FIGURES ......................................................................................................... xiii LIST OF ABBREVIATIONS .......................................................................................... xiv ABSTRACT .......................................................................................................................xv CHAPTER I: INTRODUCTION .........................................................................................1 Background ..............................................................................................................1 Research Questions ..................................................................................................5 CHAPTER II: LITERATURE REVIEW ............................................................................6 Overview of the Lungs.............................................................................................6 Anatomy and Physiology .............................................................................6 The Disease of Interest: Lung Cancer ..........................................................8 Exposures of Interest: Gender and Lung Cancer Treatment Modality ..................11 Epidemiology .............................................................................................14 Impact on Healthcare Resources ................................................................22 Origin .........................................................................................................24 Clinical Signs and Symptoms of Lung Cancer ..........................................25 Procedures for Diagnosing Lung Cancer ...................................................27 Screening........................................................................................30 Pathology/Histology ..................................................................................33 Staging/Extent of Disease ..........................................................................36

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Lung Cancer Prognosis ..............................................................................41 Lung Cancer Survival and Risk Factors ................................................................43 Gender ........................................................................................................43 Tobacco .....................................................................................................50 Race and Ethnicity .....................................................................................52 Genetics......................................................................................................53 Family History ...............................................................................55 Genetics and the Environment .......................................................56 Geographic Variation .................................................................................57 Alcohol .......................................................................................................59 Diet and Micronutrients .............................................................................60 Obesity and Body Mass Index ...................................................................63 Occupation .................................................................................................66 Hormones ...................................................................................................67 Socioeconomic Status ................................................................................68 Environment ...............................................................................................69 Diseases Associated with Lung Cancer .....................................................70 Treatments for Lung Cancer ..................................................................................71 Confined to the Lungs ................................................................................71 Local Spread ..............................................................................................72 Distant Spread ............................................................................................72

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Lung Cancer Relapse .................................................................................73 Complications of Lung Cancer ..................................................................73 Lung Cancer Treatment Modalities .......................................................................75 Radiation Therapy......................................................................................75 Chemotherapy ............................................................................................77 Surgery .......................................................................................................81 Combination Therapy ................................................................................83 Emergent Modalities ..................................................................................84 Conclusions and Assessment of the Literature ..........................................86 CHAPTER III: PROCEDURES AND METHODS ..........................................................89 Introduction ............................................................................................................89 Aims/Hypothesis ....................................................................................................89 Aim 1 .........................................................................................................89 Hypothesis 1...............................................................................................90 Aim 2 .........................................................................................................90 Hypothesis 2...............................................................................................90 Aim 3 .........................................................................................................90 Hypothesis 3...............................................................................................90 Participant Description and Case Identification ....................................................90 Variables of Interest (Inclusion and Exclusion Inclusion Criteria) .....................102 Inclusion Criteria .....................................................................................102

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Exclusion Criteria ....................................................................................104 Variable Identification and Coding ......................................................................108 Epidemiologic Research Design ..........................................................................132 Data Collection Methods .....................................................................................133 Statistical Procedures ...........................................................................................136 Study Question One .............................................................................................139 Study Question Two ............................................................................................142 Study Question Three ..........................................................................................142 Preliminary Statistical Analysis ...........................................................................143 Summary ..............................................................................................................145 CHAPTER IV: PRESENTATION AND ANALYSIS OF DATA..................................147 Introduction ..........................................................................................................147 Population Characteristics ...................................................................................151 Demographics ......................................................................................................151 Testing the Hypotheses ........................................................................................166 Hypothesis I .........................................................................................................166 Introduction ..............................................................................................166 Potential Confounders, Multicollinearity and Interaction .......................168 Multinomial Logistic Regression .............................................................172 Multinomial Logistic Regression – Main Effects ....................................178 Multinomial Logistic Regression – Interaction .......................................181

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Multinomial Logistic Regression Model Assessment .............................188 Random Effect .........................................................................................191 Overall Interaction Effect on Treatment Received ..................................193 Hypothesis I Conclusion ..........................................................................215 Hypothesis II .......................................................………………….……………216 Introduction and Survival Analysis .........................................................216 Hypothesis II Conclusion .........................................................................224 Hypothesis III.......................................................................................................224 Introduction ..............................................................................................224 CPHM Interaction Terms .........................................................................230 Residuals ..................................................................................................247 Overall Interaction Effect on Survival .....................................................250 Hypothesis III Conclusion .......................................................................270 CHAPTER V: DISCUSSION ..........................................................................................272 Introduction ..........................................................................................................272 Assessment of the Major Findings .......................................................................273 Hypothesis I .........................................................................................................274 Hypothesis II ........................................................................................................275 Hypothesis III.......................................................................................................276 Comparison and Consistency of Key Findings ....................................................278 Comparison and Inconsistency of Key Findings .................................................279

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Study Limitations .................................................................................................282 Study Strengths ....................................................................................................284 Public Health Importance ....................................................................................289 Future Directions .................................................................................................291 REFERENCES ................................................................................................................293 APPENDICES .................................................................................................................310 Appendix I: State Demographics .........................................................................311 Appendix II: Lung Cancer Distribution Tables ...................................................319 Appendix III: Chemotherapy Agents ..........…………………………………….325 Appendix IV: Calculation of the Overall Interaction Effect ............……………326 ABOUT THE AUTHOR ................................................................................... END PAGE

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LIST OF TABLES Table 1: Molecular Biomarker for Lung Cancer (LC).......................................................32 Table 2: AJCC TNM Staging System for Lung Tumors ..................................................40 Table 3: Incidence and Mortality Rates .............................................................................58 Table 4: Lung Cancer and Food Intake Cohort Studies .....................................................63 Table 5: Lung Cancer Treatment Recommendations ........................................................80 Table 6: Selection Criteria for State/State Cancer Registries ............................................92 Table 7: NAACCR Criteria for Gold/Silver Certification .................................................97 Table 8: Annual NAACCR Certification Designation ......................................................98 Table 9: Annual NAACCR Region and Certification .......................................................99 Table 10: Final NAACCR Eight State Cancer Registries ...............................................101 Table 11: NAACCR Variable Code and Description………………………………..…106 Table 12: NAACCR Code and Description of Race .......................................................109 Table 13: NAACCR Code and Description of Spanish/Hispanic Origin ........................111 Table 14: NAACCR Code and Description of Laterality ................................................113 Table 15: NAACCR Code and Description of LC Morphology .....................................114 Table 16: NAACCR Code and Description of LC Behavior ...........................................115 Table 17: NAACCR Code and Description for Grade ....................................................117 Table 18: NAACCR Code and Description Diagnostic Confirmation ............................119 Table 19: NAACCR Code and Description for Reporting Source Type .........................120 Table 20: NAACCR Code and Description for Class of Case ........................................121

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Table 21: NAACCR Code and Description for Payor at Diagnosis……………………122 Table 22: NAACCR Code and Description SEER Summary Stage 1977.......................123 Table 23: NAACCR Code and Description SEER Summary Stage 2000.......................124 Table 24: NAACCR Code and Description of Surgical Primary Site .............................125 Table 25: NAACCR Code and Description of Radiation Treatment ..............................126 Table 26: NAACCR Code and Description for Chemotherapy.......................................127 Table 27: Derived AJCC Stage Group.............................................................................128 Table 28: NAACCR Code and Description of Follow-Up Sources ................................131 Table 29: NAACCR Code and Description of Autopsy ..................................................131 Table 30: State Cancer Registry Contact Information .....................................................134 Table 31: Final Data Lung Cancer Set Variables ............................................................149 Table 32: Classification of Variables for Hypothesis Testing .........................................150 Table 33: State Cancer Registries versus Gender ...........................................................152 Table 34: Lung Cancer Distribution ................................................................................153 Table 35: Lung Cancer Distribution ................................................................................155 Table 36-a: Lung Cancer Treatment Group and State .....................................................157 Table 36-b: Total Population for the Eight States ...........................................................158 Table 37: Lung Cancer Distribution – Treatment Group vs. Gender ..............................160 Table 38: Lung Cancer Distribution – Treatment Group vs. Stage .................................161 Table 39: Lung Cancer Distribution – Treatment Group vs. Grade ................................163 Table 40: Lung Cancer Distribution – Treatment Group vs. Morphology ......................164

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Table 41: Lung Cancer Distribution - Treatment Group versus Race ............................319 Table 42: Lung Cancer Distribution - Treatment vs. Marital Status at Diagnosis .........321 Table 43: Lung Cancer Distribution - Treatment vs. Age Group at Diagnosis ...............323 Table 44: Predictor Variable and Explanatory/Independent Variables ...........................167 Table 45: Multicollinearity Assessment via Logistic Regression....................................171 Table 46: Type 3 Analysis of Effects ..............................................................................174 Table 47-a: Main Effect of Morphology..........................................................................180 Table 47-b: Main Effect of Race .....................................................................................181 Table 48: Gender and Stage Interaction Terms ...............................................................183 Table 49: Gender and Marital Status Interaction Terms ..................................................184 Table 50: Stage and Age Group at Diagnosis Interaction Terms ....................................186 Table 51: Stage and Grade at Diagnosis Interaction Terms ............................................188 Table 52: Random Effect of State ....................................................................................192 Table 53: Type III Analysis Main Effects and Interaction Terms ...................................193 Table 54: Overall Variable Effect on Lung Cancer (LC) Treatment Received ...............195 Table 55: Interaction Effect of Gender on LC Treatment Received ................................197 Table 56-a: Interaction Effect of Stage on LC Treatment Received ...............................200 Table 56-b-1: Interaction Effect of Stage on LC Treatment Received ............................202 Table 5b-b-2: Interaction Effect of Stage on LC Treatment Received ............................203 Table 56-b-3: Interaction Effect of Stage: on LC Treatment Received ...........................204 Table 56-c-1: Interaction Effect of Stage I: on LC Treatment Received .........................206

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Table 56-c-2: Interaction Effect of Stage II on LC Treatment Received ........................207 Table 56-c-3: Interaction Effect of Stage III on LC Treatment Received .......................208 Table 56-d: Interaction Effect of Marital Status on LC Treatment Received ..................209 Table 56-e-1: Interaction Effect of Grade I on LC Treatment Received .........................210 Table 56-e-2: Interaction Effect of Grade II on LC Treatment Received........................211 Table 56-e-3: Interaction Effect of Grade III on LC Treatment Received ......................211 Table 57-a: Interaction Effect of Age Groups 4 and 5 on LC Treatment Received ........213 Table 57-b: Interaction Effect of Age Groups 6 and 7 on LC Treatment Received ........214 Table 58: Lung Cancer Survival ......................................................................................217 Table 59: Survival Data for Lung Cancer Cases .............................................................219 Table 59-a: Extracted Life Table Survival Parameter Results.........................................220 Table 60: Gender Survival Estimates (in months) ...........................................................221 Table 61: Life Tables – Test of Equality over Strata .......................................................227 Table 61-a: The Cox Proportional Hazards Model (CPHM1) .........................................229 Table 62: Hazard Ratios and 95% Confidence Intervals .................................................232 Table 63: Hazard Ratios and 95% Confidence Intervals .................................................236 Table 64: Hazard Ratios and 95% Confidence Intervals .................................................240 Table 65: Hazard Ratios and 95% Confidence Intervals .................................................241 Table 66: Hazard Ratios and 95% Confidence Intervals .................................................246 Table 67: Hazard Ratios and 95% Confidence Intervals .................................................247 Table 68: Overall Effect on Survival ...............................................................................252

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Table 69-a: Overall Effect of Gender on Survival ...........................................................255 Table 69-b: Overall Effect of Gender on Survival...........................................................255 Table 69-c: Overall Effect of Morphology on Survival...................................................256 Table 69-d: Overall Effect of Morphology on Survival ..................................................257 Table 69-e: Overall Effect of Morphology on Survival...................................................257 Table 69-f: Overall Effect of Grade on Survival .............................................................258 Table 69-g: Overall Effect of Grade on Survival.............................................................259 Table 69-h: Overall Effect of Stage on Survival .............................................................261 Table 69-i: Overall Effect of Stage on Survival ..............................................................262 Table 69-j: Overall Effect of Stage on Survival ..............................................................263 Table 69-k: Overall Effect of Age Group on Survival ....................................................264 Table 69-l: Overall Effect of Age Group on Survival .....................................................265 Table 69-m: Overall Effect of Race on Survival .............................................................266 Table 69-n: Overall Effect of Treatment Type on Survival .............................................268 Table 69-o: Overall Effect of Treatment Type on Survival .............................................268 Table 69-p: Overall Effect of Treatment Type on Survival .............................................268 Table 69-q: Overall Effect of Treatment Type on Survival .............................................269 Table 69-r: Overall Effect of Treatment Type on Survival .............................................269 Table 70: Geographic Area: Florida ...............................................................................311 Table 71: Geographic Area: Idaho ..................................................................................312 Table 72: Geographic Area: Indiana ...............................................................................313

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Table 73: Geographic Area: Massachusetts ....................................................................314 Table 74: Geographic Area: Nebraska ............................................................................315 Table 75: Geographic Area: Oregon ...............................................................................316 Table 76: Geographic Area: Rhode Island......................................................................317 Table 77: Geographic Area: South Carolina ...................................................................318 Table 78: Chemotherapy Agents for Lung Cancer ..........................................................325

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LIST OF FIGURES Figure 1: The Respiratory System .....................................................................................8 Figure 2: 2006 Estimated US Cancer Cases ....................................................................20 Figure 3: 2006 Estimated US Cancer Deaths ..................................................................20 Figure 4: US Women Cancer Death Rates .......................................................................21 Figure 5: US Men Cancer Death Rates .............................................................................21 Figure 6: Structure of Morphology Code .........................................................................34 Figure 7: Structure of a Complete ICD-O Code ...............................................................34 Figure 8: ICD-O-3 Site (Lung) Codes .............................................................................35 Figure 9: Lung Anatomy with ICD-O-2/3 Codes ............................................................35 Figure 10: State Selection Process ....................................................................................93 Figure 11: Residual Analysis: Treatment Groups I, II, III ...............................................189 Figure 12: Residual Analysis: Treatment Groups IV, V..................................................190 Figure 13: Residual Analysis: Treatment Groups VI, VII ...............................................191 Figure 14: Life Table Method ..........................................................................................218 Figure 15: Cumulative Hazard Function (CHF) ..............................................................222 Figure 16: Transformation of the CHF ............................................................................223 Figure 17: Residual Testing of the Lung Cancer Distribution.........................................249 Figure 18: Residual Testing of the Lung Cancer Distribution .........................................250

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LIST OF ABBREVIATIONS ACR

American Cancer Society

ATBC

Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study

BMI

Body Mass Index

CARET

beta-Carotene and Retinol Efficiency Trial

CI

Confidence Interval

CO2

Carbon Dioxide

DHEW

Department of Health, Education and Welfare

ICD-9

International Classification of Disease: 9th Edition

ICD-10

International Classification of Disease: 10th Edition

LC

Lung Cancer

NTLDRI

National Tuberculosis and Lung Diseases Research Institute

OR

Odds Ratio

PHS

Physicians' Health Study

SAS

Statistical Analysis Software

SEER

Surveillance, Epidemiology, and End Result Program

SES

Socioeconomic Status

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ABSTRACT

The objectives of this research were to test treatment and survival differences between women and men with lung cancer as there is minimal investigation in the literature. Three research questions were developed with statistical testing for gender differences based on similar cancer type, stage, treatment assignment and survival. Data for 44,863 primary lung cancer cases were collected from eight U.S. state-based cancer registries to investigate the research questions. The lung cancer incidence data included the morphological cell-types of adenocarcinoma (AC); squamous cell carcinoma (SCC); large cell carcinoma (LCC) and small cell carcinoma (SCC). Stage, grade, treatment type, as well as, individual characteristics such as gender, age at diagnosis, marital status at diagnosis and race were other variables obtained to be included in the statistical models. Reporting the overall effect for lung cancer gender specific treatment differences or survival has not been demonstrated in the literature to the author’s knowledge. By convention, main effects and interaction effects are reported in the literature; without including an evaluation the overall effect of a variable on the outcome, possible misinterpretations could be made. For example, utilizing the Cox’s Proportional Hazards model when the interaction effect of gender and treatment type received was examined, females were at an increased risk for death by as much 29% as compared to males (HR = 1.18, 95% CI 1.09 – 1.29). But when the gender effect on survival was assessed, there was an increase in females survivorship as compared to males by as much as 28% (HR = xv

0.80, 95% CI 0.72 – 0.97 ). In conclusion, by using a unique statistical approach, statistically significant Odds Ratios and Hazard Ratios were demonstrated for the research data set when the overall interaction effect on the outcome was examined. Recommendations to health care practitioners include adhering to current guidelines, e.g. American Medical Association, for lung cancer treatments. Standard treatment protocols were not always followed for early stage disease, e.g. females versus males with stage I lung cancer were 1.71 times more likely to receive chemotherapy in combination with radiation therapy versus a standard first treatment course of surgery (OR = 1.71, 95% CI 1.06 – 2.78). Also, depending on the lung cancer morphology and lung cancer treatment, females as compared to males could exhibit an increase in survivorship by as much as 28%. To improve the results of medical care decisions for lung cancer, clinicians may find the information presented in this study useful and encourage further research on which treatment increases survival for both men and women.

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CHAPTER I: INTRODUCTION Background There are many histological types of lung cancer and finding an optimum treatment regimen is a challenge. Lung cancer typically is classified into two major divisions: small cell lung cancer (SCLC or oat cell carcinoma) and non-small cell lung cancer (NSCLC) 2-5. SCLC accounts for approximately 20% of all the lung cancer cases, whereas about 80% of all lung cancer cases are NSCLC. There are many types of NSCLC but the three major histological classifications are adenocarcinoma, large cell carcinoma, and squamous cell carcinoma 6, 7. The treatment modalities for small cell lung cancer versus non-small lung cancer are different due to the biological response of the particular cancer cell type to various treatment regimens 8-11. The medical interventions for each histological type can include any combination of treatment modalities such as surgery, radiation therapy, and/or chemotherapy. Adding to the complexity of lung cancer is that the incidence, prevalence, and survival rates are also dissimilar for the specific histological type 1, 2, 12, 13. One prognostic factor for lung cancer ―in terms of treatment treatment/modality received‖ that requires further exploration is the relationship between lung cancer treatment(s) and gender. There is limited research regarding if the treatment modality, e.g. radiation therapy, surgery, chemotherapy, received is dependent upon being a woman with lung cancer as compared to a man with lung cancer. This is of particular interest because of all the various types of cancers and treatments available, lung cancer has

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become the leading cause of death for women as there has been a 600% increase for women with lung cancer from 1930 to 1997 44. Any effect which gender exerts in the decision regarding which lung cancer treatment modality decided upon must be disentangled from other prognostic factors. The study question(s) of this research attempted to enumerate the risk of being a woman with lung cancer and type of treatment received compared to a man with lung cancer and the type of treatment modality that he receives. An assessment was made to determine if a statistically significant association between gender and treatment modality exists. Another aspect of gender differences that was investigated included the impact on survivorship between women and men with lung cancer. Stratified analysis was based on the histological type, stage, grade, gender and the treatment modality or treatment modalities received in an attempt to investigate treatment effects on survival. Much of the scientific literature on lung cancer research does not address survival and the relationship gender has to play due to the effects of specific histological lung cancer types, stage or progression of LC, and grade on gender and survival. The purpose of this research study is to provide a quantitative assessment of the outcome (survival) for women as compared to men based on the particular treatment received for lung cancer 14-17. Minorities will also be included in the subject selection; it is important not to exclude minorities as they can provide valuable epidemiologic information. In an attempt to facilitate minority research, United States government agencies, e.g. the National Cancer Institute (NCI), now mandate the inclusion of women, children, and

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minorities if government funding is provided for the study 18-20. For the purposes of this research, minorities are being included since the treatment modality selected for the treatment of lung cancer may be dependent upon race as well as gender 21. In other words, race may or may not play a role in the treatment modality utilized for lung cancer. Although this research is primarily focused on what treatment modalities are utilized for men as compared to women, the impact of gender on lung cancer survival will also be considered. There are several reasons why is gender important as risk factor for lung cancer. First, according to the 2001 report by the Surgeon General 44, female lung cancer mortality increasing by 600% since 1930 noting that this is a ―full blown epidemic‖ 44. Secondly, the causal pathways of lung cancer development are blurred for women 8, 13. For example, the causes of lung cancer among women seemingly different from men, are still not resolved 3, 8, 13. One possible answer to this question is much of the current knowledge and treatment patterns for lung cancer are based on research primarily done on men. Previously, the association between being a woman and the risk of lung cancer was considered negligible as reported in the 1964 Report of the Advisory Committee to the Surgeon General DHEW Publication Number PHS 110323. But as behavior and other temporal changes, such as cigarette smoking have occurred over the past several decades for women, lung cancer incidence and increased mortality rates of lung cancer 24, 76, 77. Women historically have been excluded from clinical trials or if included, the data was not analyzed22. If women are at a greater risk for lung cancer than men at the same level of smoking one result of women being excluded historically from

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research studies is even there is no evidence in the literature to support this; results have been conflicting and limited 22. A 1964 report of the ―Surgeon General on Women and Smoking‖ 23 did not reach any conclusions concerning what role gender difference may play in the development of lung cancer. The 1964 report did conclude that although smoking was risk factor for lung cancer in men, smoking was not a risk factor for lung cancer in women as there was not enough scientific evidence to establish causality 24. What was not known at the time of the 1964 report was the temporal effect due to when women started smoking on a large scale and the development of lung cancer (lag time of approximately twenty years). Hypotheses have been developed based on possible physiological responses to carcinogens and hormonal related differences in women as compared to men but inconsistent results in the literature remains 8, 15, 17, 25-27. Lung cancer is the leading cause of death of all cancers in both men and women in the United States 28. The overall lifetime risk in women is 1 in 17 and for men 1 in 13 for lung cancer development 27, 29, 30

. Lung cancer has an extremely low 5 year survival rate of 15%. The primary cause of

lung cancer is due to smoking cigarettes; smoking is estimated as being a causal factor in 80 – 90 % of all lung cancer cases 14, 15. Some of the literature reports the susceptibility for lung cancer in women is different when compared to men with women by demonstrating an increased risk for lung cancer

8, 14, 15, 31, 32

. Another source for concern

for women is second hand smoke; of the individuals that die from that exposure, 65 % are women 33. This could possibly indicate hormonal differences may make women more

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susceptible to smoke. Sex differences in survival and susceptibility have been linked to estrogen as a lung cancer risk factor 229.

Research Questions Given what present day research has and has not found concerning the treatment of lung cancer and the role gender has played in the selection of the treatment modality, the following research questions will be addressed in this dissertation: Question One: Do men and women with the same histological type and stage/grade of lung cancer receive the same treatment modality? Question Two: Are there differences in survival between men and women regardless of the treatment modality received? Question Three: Do men and women with the same histological type, stage/grade of lung cancer, and same treatment modality, have comparable survival?

As stated in the abstract and in the background, the study or research question(s) will focus on the association between the treatment received by women with lung cancer as compared to that received by men. The study will investigate the overall survival patterns based on the treatment that a woman with lung cancer receives versus a man.

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CHAPTER TWO: LITERATURE REVIEW Overview of the Lungs Anatomy and Physiology The lungs, part of the respiratory system, are coned shaped, sponge-like, and highly elastic organs located in the chest. The functions of the respiratory system and in particular, the lungs, include gas exchange, moisturizing the inhaled air, stabilizing the temperature of all air to body temperature, and filtering harmful substances 34, 35. As shown in Figure 5, the respiratory system includes the nasal cavity, the windpipe or trachea, and two lungs. The upper tract of the respiratory system includes the mouth or oral cavity, the nasal cavity, and the trachea 36. The lower tract of the respiratory system consists of lungs, bronchi, and alveoli. Inspiration and expiration are the two phases of respiration or breathing. During each phase of respiration, the volume or dimensions of the chest cavity is changed, i.e. increased lung volume (inspiration) or decreased lung volume (expiration) 36, 37. Air entering into the body via the nose or mouth, contains approximately 21% oxygen with no carbon dioxide. The air is drawn into the trachea and bronchi, and then enters the lungs through the left or right bronchi. Air entering into the main branch of the bronchi will travel into smaller bronchi which further divide into smaller, complex tubes called the bronchioles 36, 37. Mucus is secreted by the inner lining of the larger bronchial tubes. One of the purposes of the secretion is to filter or trap dirt from the air. In a continuous, sweeping process, the mucus is expelled from the lungs by cilia; cilia are

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similar to hair or brush-like structures 38. Coughing is another method by which mucus is removed from the lungs. The final or most distal ends of the bronchioles are connected to small air sacs called alveoli. The exchange of gases occurs in the alveoli. T he alveoli are very thinly walled, balloon-like structures that expand upon inspiration and relax or deflate upon expiration 37, 38. Each alveolus is surrounded by small blood vessels called capillaries. When the concentration of dissolved oxygen is greater in the alveoli than in the capillaries, oxygen diffuses across the alveoli walls into the blood plasma contained in the capillaries. An increased concentration of CO2 in the blood results in carbon dioxide diffusing from the capillaries into the alveoli. At the time air is exhaled, it contains approximately 16% oxygen and 4.5% carbon dioxide 37, 38. As previously described, the exchange of oxygen and carbon dioxide occurs in the lungs. Each lung is identified by the apex, lobes, and base. The left lung is comprised of 2 lobe or sections; typically weighing 625 grams 34, 39. The right lung has three lobes, approximately 567 grams. The left lung is smaller than the right to accommodate the heart and other structures in the mediastinum. The lungs have a surface area approximately equal to the size of a tennis court and while at rest, the entire body blood supply or blood volume, five liters, passes through the lungs each minute 38.

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Figure 1: The Respiratory System Source: webschoolsolutions.com/patts/systems/lungs.htm

The Disease of Interest: Lung Cancer Any obstruction of air flow through the bronchial tree or at the alveoli can cause serious functional limitations or even death 37, 38. Besides the various diseases which can obstruct airflow and affect the cellular respiration, the lungs can become cancerous 10. Physiological changes in the lung tissue where the lung becomes cancerous can be defined as an uncontrolled cell growth in the lung forming clumps of tissue referred to as malignant tumors 37. Exposure to carcinogens, such as those present in tobacco smoke, immediately causes changes to the tissue lining the bronchi of the lungs (the bronchial mucous membrane), the more cumulative damage to the lung tissue, the greater the probability a tumor will develop 9, 10. The non-small cell lung cancers (NSCLC) are grouped together because their prognosis and management is roughly identical 2, 9, 54.

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There are 3 major subtypes of NSCLC: squamous, large cell, and adenocarcinoma 1, 2, 55. Squamous cell carcinoma starts in the larger breathing tubes but grows slower, this means that the size of these tumors vary when the diagnosis is made. Adenocarcinoma (the slower growing type forms alveolar cell cancer) starts near the gas-exchanging surface of the lung 56. It is less closely associated with smoking. Large cell carcinoma is a fastgrowing form that grows near the surface of the lung 4, 57. It is primarily a diagnosis of exclusion, and when more investigation is done, it is usually reclassified to squamous cell carcinoma or adenocarcinoma 56. Small cell carcinoma (SCLC, also called "oat cell carcinoma") is the less common form of lung cancer. Approximately 20% of all primary lung cancer diagnosed are small cell lung cancer and account for 30,000 to 35,000 cases per year in the United States 13, 28. Small cell LC tends to start in the larger breathing tubes and progresses rapidly becoming quite large 6, 10, 58, 59. SCLC is more sensitive to chemotherapy, but carries a worse prognosis and is often metastatic at presentation 2, 3, 33. This type of lung cancer is strongly associated with smoking 4. Exposure to carcinogens, such as those present in tobacco smoke, immediately causes cumulative changes to the tissue lining the bronchi of the lungs (the bronchial mucous membrane) and the more tissue that gets damaged, the greater the probability a tumor will develop 4, 37. Squamous cell carcinoma usually is diagnosed after the disease has spread 1, 5, 12, 13. The overall prognosis for all non-small cell lung cancers is poor, with a five-year survival rate of about 15% 11, 13, 60. The survival rate is higher (close to 50%) when the cancer is detected and treated early 13. Survival rates after surgery vary 7, 43, 54, 61-63. For those with stage I

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disease, the five-year survival rate is about 47% 13, 64. For those with stage III disease, the five-year survival rate is 8% 2, 13, 33, 64. Even when surgery and other therapies are initially successful, there is a high risk of the cancer reoccurring 4, 27, 32, 65. This reflects the fact that squamous cell carcinoma is rarely restricted to just one area. Squamous cell carcinoma readily spreads to other parts of the body 4, 30, 66. Cancer is a multistep progression of changes or phases that occur in the genes 43, 52, 67-71

. The genotypic changes are characterized by the loss of normal cellular

differentiation and an alteration in tissue morphology due to an increase of unrepaired DNA damage and the formation of abnormal genomic variants 10. Lung cancer can result from an exposure of a susceptible host to carcinogenic agents; these exposures cause progressive changes in the cell from metaplasia, to atypia and dysplasia, then developing into a carcinoma in situ and invasive cancer 72. The changes that occur on the cellular level are variable from individual to individual, and not all neoplasms follow the same progress 4. Metaplasia, the first phase of cancer development, is the transformation of a mature differentiated cell type into a different mature differentiated cell type 4. This transformation is in response to an injury or insult at a cellular level which can make the tissues more susceptible to a malignant alteration. Atypia is defined as an abnormality associated with a precancerous process. An atypical cell (atypia) can also be an indication of an infection or irritation 4, 37. Atypia can be caused by a chronic irritation and this has been shown increases the probability of premalignant lesions 9. Dysplasia is typically an irreversible condition or change in the cell that is a precursor of invasive

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epithelial tumors. There levels or grades of dysplasia and high grade dysplasia can be difficult to distinguish from carcinoma in situ during histologic examination 4, 37.

Exposures of Interest: Gender and Lung Cancer Treatment Modality There is limited research regarding the survival of women with lung cancer and the treatment received compared to the survival of men with lung cancer and the treatment men receive 12, 40-45. Presently, there are no quantitative results that show whether there is a statistically significant difference regarding survival due to a particular treatment for women as compared to men having the same histological type and stage of lung cancer 46-48. The goal of this research is to investigate the exposures, gender and treatment modality and their effect on the outcome, survival. Several research questions must be answered in order to evaluate the relationship between these variables. Belani et.al., 2007, in the article ―Women and lung cancer: Epidemiology, tumor biology, and emerging trends in clinical research‖, noted that ―emerging findings in the scientific literature reveal gender specific differences in cancer prognosis‖ 41. The authors expressed an urgent need to increase research and funding to improve lung cancer care, women in particular 41. Ringer et al. (2005) in the article "Influence of sex on lung cancer histology, stage, and survival in a Midwestern United States Tumor Registry." identified differences between men and women with regard to lung cancer type, stage at diagnosis, and survival in a single hospital system cancer registry. The study design was a retrospective cohort with a target population based on case information from a lung

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cancer tumor registry at a single hospital system composed of 2 independent hospitals in the Midwestern United States27. This database included all patients from 1996 to 2002 with known lung cancer or abnormal findings on chest radiography or computed tomography (N=2618). Patients with adenocarcinoma or squamous cell, small-cell, or large-cell carcinoma were included in the study. A total of 1216 men and 997 women were included in the study. The authors found no significant difference in age between sexes at diagnosis27. Women were significantly more likely to have adenocarcinoma or small-cell carcinoma but less likely to have squamous cell carcinoma compared with men. There were no significant differences between sexes in the incidence of large-cell carcinoma. No significant differences were found between men and women in terms of cancer stage at diagnosis 27. There were significant differences in survival between the histologic types at years 3, 4, and 5. Only patients with stage I disease showed a difference between sexes and only for years 2, 3, 4, and 5. This study did not investigate the impact of treatment modality on survival, gender, histological type and stage of lung cancer. Women were found to have a decreased survival with late stage lung cancer as compared to men 27 but there was no expansion of the results based on the type of treatment received for women and men. In the article by Ouellette, et. al. (1998), ―Lung Cancer in Women as Compared to Men: Stage, Treatment, and Survival‖ 8, gender differences in survival were examined. The authors8 cited several articles that reported on cardiovascular disease and the survival advantage for men as compared to women; Ouellette, et. al.’s research attempted to

12

identify gender disparities in lung cancer survival. To test the hypothesis of a gender difference in lung cancer survival, a retrospective cohort study of 104 women and 104 men was conducted. Women were found to have a higher incidence of small cell lung cancer (25% versus 12% as compared to men); whereas men had a greater percentage of squamous cell carcinoma (51% versus 38% as compared to women) 8. The authors noted there were no statistically significant survival differences between women and men but women were found to live, on average, 6 months longer then men (mean survivalwomen = 24 months, mean survivalmen = 18 months). Ouellette, et. al. reported when stratified analysis based on the stage of lung cancer (Stage I, II, IIIa, IIIb, and IV) was assessed, ― these two groups with a coefficient according to stage, there was a survival advantage in women, and they seem to live 12 months longer than men‖ 8. The authors reported that this increase in women’s survivorship may be contributed to an intrinsic factor, e.g. hormones. Ouellette, et. al. concluded the overall survival between men and women was not statistically significant but that there was a significant survival difference between men and women with lung cancer when stratified on stage. The question about gender differences and lung cancer survival has not been resolved in the literature as conflicting results still exist 40, 41, 49-52. A recently published article investigating gender differences and survival by Wisnivesky and Halm, 2007, ―Sex-Differences in Lung Cancer Survival: Do Tumors Behave Differently in Elderly Women‖ examined women’s responses to treatment and their survival as compared to men 53. The study was based on SEER data collected from men and women diagnosed

13

between 1991 and 1999 (N = 18,967) with stage I and stage II non-small cell lung cancer. It was shown that for early stages of lung cancer, that women have a better overall and relative survival as compared to men (p = 0.001). The authors noted women as compared to men had a greater probability of being diagnosed with adenocarcinoma, tended to be diagnosed at an earlier age, and when the disease had not metastized (localized) 53.

Epidemiology Epidemiology is utilized to monitoring the consequences of an intervention and is used in the development of hypotheses for risk factors 73, 74. Epidemiological methods are used to study lung cancer for the identification of the disease frequency, determinants, and distribution of lung cancer in human populations 73, 74. For example, there has been an increase of 600% in mortality for women with lung cancer since 1930 28, 40, 60 and without monitoring or the identification of the disease frequency in epidemiological terms ―this epidemic rise in lung cancer mortality44‖ 24, 75-77 may not have been identified. Alberg et. al, (2005) reported that in the 20th century of the United States the lung cancer epidemic ―peaked and began to declined by century’s end, a decline that continues today‖ 40

. The rates of lung cancer in women were shown to have a differential increase in lung

cancer incidence and mortality over time as compared to men 40. Lung cancer rates have peaked for men but the rates for women are still increasing in many regions of the world 5, 16, 30, 65, 78, 79

. While the gap between lung cancer gender differences is narrowing, the

differences for in incidence and mortality rates are declining 45, 46, 66, 80, 81. According to

14

the International Agency for Research on Cancer (IARC), rates of all lung cancer types among women and adenocarcinoma of the lung in men continue to rise in many Western countries 5. Worldwide, lung cancer is the 10th leading cause of death and is the leading cause of death for all types of cancer 5. The 5-year relative survival remains low; approximately 10% in Europe. In Developing Countries, the incidence of smokingrelated lung cancer is rising rapidly 5, 30, 82. Countries such as China are expected to see a marked increase in lung cancer cases as smoking is exceedingly common 5, 78. Devesa and Bray, 2005, reported recent total lung cancer incidence rates among males varied by 4-fold, from 83.6 among U.S. Blacks to 21.1 in Sweden 30. Rates in the Nordic countries, which varied by 2-fold from a high in Denmark to a low in Sweden, still were generally lower than in other parts of Europe, where the incidence rate was highest in the Netherlands 30. Lung cancer rates in Italy, Slovenia and France were higher as compared to U.S. Whites or Canadian LC incidence. The authors also noted that among females, recent incidence rates varied by almost 8-fold, with the highest among U.S. Blacks (35.8) and the lowest in Spain (4.6) 30. The ranking of rates among females paralleled that in males, with the exception of Switzerland. Lung cancer rates everywhere were higher among males than females 30. Male to female rate ratios varied from less than 2 in Iceland, U.S. Whites, Canada, Denmark and Sweden to more than 6 in Slovenia, Italy, and France and more than 10 in Spain 13, 30. Henschke et. al. (2006) reported that the US cancer rates for men and women in their research showed a dose (pack-years) – incidence (lung cancer) threshold as there was a biological gradient associated with increased pack-

15

years with an increased risk of lung cancer 174. In the United States, the American Cancer Society estimated that there were 92,305 new cases of lung cancer in men and 79,544 new cases among women in 2006 2. The majority of cancer deaths among women and men are attributed to lung cancer 2. According to the American Cancer Society, approximately 60% of newly diagnosed lung cancer cases die within the first year of diagnosis 2. The 5- year relative survival rate is approximately 15% in the United States. The prevalence rates of smoking as reflected in the National Health Interviews, Current Population Survey, notes that smoking attributable cancer mortality for males is approximately 90% and 78% for females 84-86. Current literature about smoking habits (age when started smoking, number of cigarettes daily, duration frequency of inhalation, use of dark tobacco, and non-filter cigarettes) 8789

, notes that a smoker is twenty two times more likely to die from lung cancer than a

nonsmoker 86. In Chapter Three ―The Descriptive Epidemiology of Lung Cancer‖ from the book Epidemiology of Lung Cancer: Academic Press; 1998, a study from the Saskatchewan Cancer Foundation (a population based cancer registry) was referenced by Thomas J. Mason. He noted endogenous and exogenous factors may contribute to the development of primary lung cancer in women 83. Endogenous factors can be produced or can be synthesized within an organ in the body; exogenous factors are agents or factors from outside the body (cigarette smoke) 37. Zang and Wynder conducted a hospital-based prospective, case-control study on data collected from 1995 through 1995 that included 21,057 males as controls and 14,448

16

female controls that were originally diagnosed with non-smoking related diseases 81. The authors found that at the same level of lifelong exposure to cigarette smoke, women had a 1.5 times greater risk of developing lung cancer as compared to men 90. There was a statistically significant difference in the incidence of adenocarcinoma; females were at a higher risk of developing adenocarcinoma versus males independent of tar yield per cigarette 90. Zang and Wynder noted a statistically significant difference between squamous cell carcinoma for women as compared to men, dependent upon the level of total tar per cigarette (> 6 kg). Women were found to develop primary lung cancer at earlier age as compared to men, yet women smoked fewer cigarettes for a shorter time than men 81. Smoking patterns have changed over the past thirty years and the change in the dominant histologic lung cancer classifications, possible differences between gender emerges 83. Lung cancer has a multivariable etiology and there are specific risks associated with the type of lung cancer 3, 91-95. These secular trends can provide a clue to the understanding of lung cancer and future research for the impact on diagnosis, treatment, and outcome 48. Other studies that identify patterns of risk by the histologic types include an article by Devesa, et al., 2005, utilizing data from the International Agency for Research on Cancer (IARC) databases 30. Morphology-specific incidence data noted that the rates of all lung cancer types are increasing for women and adenocarcinoma is rising for men 30. This trend continues even with the decrease in prevalence of smoking and the use of filtered and low tar cigarettes 13. These finding are

17

consistent with current literature as the secular trends in histologic type with the annual rise in the incidence of adenocarcinoma 10, 96-98. Govindan et al., 2006 in the article ―Changing Epidemiology of Small-Cell Lung Cancer in the United States over the Last 30 Years: Analysis of the Surveillance, Epidemiologic, and End Results Database‖, found that the proportion of women with SCLC increased from 28% in 1973 to 50% in 2002 99. When SCLC was compared to all lung cancer histologic types there was a decreased of SCLC from 17% in 1986 to 13% in 2002 99. The authors also noted that although there was an overall decrease in small cell carcinoma, survival had not improved. Stockwell, et al., 1990, found the histological type of lung cancer varied by age, sex and the use of cigarettes; this was based on observations from a population based cancer registry in Florida 96. A dose threshold for the amount of cigarettes smoked and the risk of lung cancer was not statistically significant. The authors noted that adenocarcinomas were more frequent in the younger aged population (< 60 years of age) for both genders. Men who smoked had a higher risk for squamous cell carcinoma whereas females very more at risk for small cell carcinoma 96. Adenocarcinoma was the most frequently encountered histological type for women who were nonsmokers 96. As there are differences in the incidence of histologic lung cancer types based on smoking patterns, the rates of incidence and mortality for lung cancer differ according to regional areas across the United States 4. Geographic mapping of lung cancer incidence and mortality was introduced by Mason in the 1960’s while at the National Cancer Institute in Atlanta, Georgia 83. This novel approach allowed for the

18

identification of regional differences in lung cancer rates; with this information Public Health resources were directed to areas with increased rates for purposes of prevention and monitoring of trends. There are differences in smoking attributable risk between males (>90%) and females (<80%) although ratio between male smoking rates and female smoking rate approach unity 83. These homogeneous and heterogeneous patterns of lung cancer etiology require further identification of factors other than smoking as to quantify the risk for the four major histologic types of lung cancer. Currently, lung cancer incidence in women is approximately equal to that of men as reported by the American Cancer Society (see Figure 6 below) 2. Lung cancer in the United States is the most common cause of cancer death in women; today the mortality rate is more than two times what it was 25 years ago 2. In Figure 7 below, the estimated number of U.S. lung cancer deaths is given for 2006. Cancer of the lung and bronchus is the most common and most fatal cancer in men (31%), followed by prostate cancer (10%), and colon & rectum cancer (10%) 2. The major killer of women from a cancer specific cause is lung cancer (27%), breast cancer (15%), and colon & rectum (10%) are the leading sites of cancer death 2. As women began smoking in increasing numbers during the 1930’s and 1940’s, the death rate due to lung cancer steadily increased with a dramatic rise in mortality rates in 1965 (see Figure 8) 2-4, 79, 100, 101. Lung cancer mortality rates in women have reached a plateau since 1998 102. As shown in the Figure 9 below, the death rate from lung cancer appears to have peaked in 1990 for men 2. The age-adjusted lung cancer death rate in

19

men has been decreasing since 1990. Prior to 1990, the major increase in cancer death rates for men was attributable to lung cancer 2. When comparing the mortality rates between men and women (Figure 8 and Figure 9), the temporal effect of gender specific smoking patterns associated with the increase in lung cancer mortality is clearly demonstrated 2, 102.

2006 Estimated US Cancer Cases* Men 710,040

Women 662,870

Prostate

33%

32%

Breast

Lung and bronchus

13%

12%

Lung and bronchus

Colon and rectum

10%

11%

Colon and rectum

Urinary bladder

7%

6%

Uterine corpus

Melanoma of skin

5%

4%

Non-Hodgkin lymphoma

4%

Non-Hodgkin lymphoma

Kidney

3%

Leukemia

3%

Oral Cavity

3%

Pancreas

2%

All Other Sites

4%

Melanoma of skin

3%

Ovary

3%Thyroid 2%

Urinary bladder

2%

Pancreas

21%

17%

All Other Sites

*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder. Source: American Cancer Society, 2006

Figure 2: 2006 Estimated US Cancer Cases

Figure 3: 2006 Estimated US Cancer Deaths

20

Cancer Death Rates*, for US Women, 1930-2001 100

Rate Per 100,000

80

60 Lung & bronchus

40

Uterus Breast Colon & rectum Stomach

20

Ovary

2000

1995

1990

1985

1980

1975

1970

1965

1960

1955

1950

1945

1940

1935

Pancreas 1930

0

*Age-adjusted to the 2000 US standard population. Source: US Mortality Public Use Data Tapes 1960-2001, US Mortality Volumes 1930-1959, National Center for Health Statistics, Centers for Disease Control and Prevention, 2004.

Figure 4: US Women Cancer Death Rates

Cancer Death Rates*, for US Men, 1930-2001 100

Rate Per 100,000 Lung & bronchus

80

60 Stomach Prostate

40

Colon & rectum

20 Pancreas

2000

1995

1990

1985

1980

1975

1970

1965

Liver 1960

1955

1950

1945

1940

1935

1930

0

Leukemia

*Age-adjusted to the 2000 US standard population. Source: US Mortality Public Use Data Tapes 1960-2001, US Mortality Volumes 1930-1959, National Center for Health Statistics, Centers for Disease Control and Prevention, 2004.

Figure 5: US Men Cancer Death Rates

There are many other aspects of the association of the risk factor (gender) to the outcome of lung cancer. These aspects of a woman’s overall susceptibility to lung cancer can include but are not limited to: smoking patterns (cigarette type, depth of inhalation, number of pack years), gender, occupation, dietary factors, nutrition, hormonal factors, air pollution, obesity, and radiation effects 45, 49, 62, 67, 81, 103-110. Incidence and prevalence

21

rates are used in the evaluation of the overall disease (lung cancer) trends. These statistics are available on government sponsored data bases such as the National Cancer Registry SEER registries, state cancer registries, e.g. members of the North American Association of Central Cancer Registries, Inc. or other state registries, such as the Washington State Department of Health Occupational Mortality Data Base 1, 13.

Impact on Health Care Resources Lung cancer has a devastating impact an individual’s quality of life but has also has direct and indirect costs associated with lung cancer diagnosis and treatment. Some of the direct costs are medical care which include hospitalization, doctors visits, home health care, hospice care, and treatment modalities such as radiation therapy, surgery, and chemotherapy 10. Direct non-medical costs associated with lung cancer can consist of transportation to and from the hospital/physician’s office, housekeeping services and any additional costs incurred due to changes necessary in the living conditions of the patient. Other considerations are the indirect costs which can be difficult to grasp the scope of exactly what can be involved with patient care 111-113. These costs such as time spent seeking medical attention, time lost from work (lost productivity), or job replacement costs cannot be directly measured in some instances 111-113. The costs associated with lung cancer care are enormous according to the National Heart Lung & Blood Institute (NHLBI). In 2003, there were 1.5 million deaths representing 47% of all deaths in the United States 111, 113, 114. These deaths were as result mainly of three disease processes; lung, cardiovascular, and blood diagnosis. By 2006, these three diseases are expected to 22

exceed $560 billion of medical costs 115. Lung cancer costs in 2004, shows medical expenditures as approximately 10 billion annually, according to the Centers for Medicare and Medicaid Services (CMS) 115. Lung cancer represents over 13% of the total cancer care costs for 2004. The non medical total or personal care exceeded 250 billion for the same time period. Lung cancer is one cancer which is more expensive to diagnose and treat because of the histologic types 64, 116-118. Many successfully treated cancer types have an early detection program or screening program for the early diagnosis of cancer 4, 117, 119. Unfortunately, lung cancer does not have an effective screening tool and typically is not diagnosed until it has spread outside the diseased organ 64, 116-118. As the majority of lung cancer is diagnosed at later stages, the associated healthcare costs and resources required are increased 111, 113. In 2007, it is estimated that the total healthcare cost (HCC) for lung cancer will be 21 billion. According to CMS, lung cancer care and treatment accounts for 10% of the total US healthcare costs 115. The United States Federal Office of the Actuary estimates that by 2016 every 20 cents of every dollar will go towards health care by 2016 115, 120

. The annual forecast by a division of the Centers for Medicare and Medicaid

Services (CMS) predicts a 10-year increase of approximately 2 trillion dollars (2.1 trillion to $4 trillion) for spending on health care in the United States 115, 120. This represents an ever increase amount of healthcare resources going to the detection and treatment of lung cancer. Using the estimated 4 trillion which is expected in 2016 with the total HCC being 400 billion, the lung cancer portion of 13.3% would make the projected lung cancer

23

portion over 53 billion 115. If the same relationship as seen in medical costs, estimated non medical costs could exceed over 1 trillion. The projected lung cancer incidence and mortality rates are expected to increase as 77 million baby boomers will move into their 60’s; the age that has the highest risk for lung cancer incidence. This march to retirement of those who were heavy tobacco use will be responsible for even higher costs after 2016 115, 120

.

Origin The site of origin of lung cancer refers to the type of tissue from which the cancer cells develops 9, 121. Lung cancer is categorized by site of origin into hilar and peripheral types; as the structures where the disease originates are different 37. The majority of early cancers in the hilar region are squamous cell types, whereas many early stage lung cancers in the peripheral areas are adenocarcinoma 121. Adenocarcinoma originates in glandular tissue; whereas a carcinoma originates in the tissue that lines the organs and tubes of the lungs called epithelial tissue 122. NSCLC adenocarcinoma and large cell carcinoma typically are located in the peripheral of the lungs and can present as solitary nodules or masses 37. Squamous cell carcinoma and small cell carcinoma are normally found in the central portion of the lungs and can be misdiagnosed as a collapsed lung (atelectasis) or pneumonia (an inflammation of the lungs) 37. Small cell carcinoma is normally located in the mainstem bronchi; this cancer originates in the Kulchitsky’ cells which are a component of the bronchial epithelium 35.

24

The tissue layers are comprised of cells that are similar in their structure and perform common functions. The intercellular material, e.g. RNA, DNA, are contained within the cells; genetic material is found within the intercellular material for that cell type. As a human embryo develops, three primary germ layers provide the basis for body tissue and organ formation 35. The three germ layers are the ectoderm, the endoderm, and the mesoderm. The ectoderm and endoderm layers are considered epithelial tissue. The epithelial tissue from the endoderm lines the respiratory tract (the lungs and the air passageways from the pharynx to the lungs).

Clinical Signs and Symptoms of Lung Cancer Early detection of cancer is credited with an increased survival; unfortunately for lung cancer, there is no early detection program that has clinically proven long-term success 4, 117, 118, 123. One impact due to the lack of early detection for lung cancer is that lung cancer has become one of the most lethal of all cancers; mortality rates for lung cancer have surpassed colorectal, breast and prostate cancer combined 4. The main difficulty in the diagnosis of lung cancer is that the majority of lung cancer cases do not have symptoms (asymptomatic) until the disease has progressed to an advanced stage 4, 64, 119, 124

. It is estimated by the American Cancer Society that only 15% of all lung cancer

cases are diagnosed in the early stage, i.e. Stage I 4. The average five year survival rate for lung cancer patients is 15%, this low survival rate is consistent with the current lack of an early diagnosis program 4, 64, 119, 124.

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Common clinical lung cancer symptoms include a new or persistent cough, hemoptysis or blood in the sputum, chest pain, wheezing, hoarseness, shortness of breath, and repeated respiratory infections, e.g. bronchitis, pneumonia 4. The symptoms of lung cancer can vary according to the tumor type and the extent of the disease or metastases. Recent articles in the literature have identified another area of concern in the diagnosis of lung cancer 4, 9, 125-128. Lung cancer diagnosis is currently done based on the symptomatic criteria outlined in textbooks that were written ten to twenty years ago 125. As physicians may not be aware of the changing patterns of lung cancer, this may add to the difficultly of a diagnosis, let alone an early diagnosis 125. Collins, et. al., 2007, noted that there have been epidemiologic changes or differences in the lung cancer patient population 4. Some of the current differences include an increased number of females with lung cancer; the most frequently encountered histological lung cancer type for males and females has changed to adenocarcinoma, and temporal differences in the age of diagnosis 4, 10. These epidemiologic differences may decrease the identification of specific symptomatic patterns in lung cancer cases which in turn could negatively impact the rate of early diagnosis 4. Approximately thirty to forty percent of lung cancer cases that are diagnosed have symptoms of metastatic disease 28; some of the most common organs that the cancer spreads to are the liver, the brain, the bones, spinal cord, and the adrenal glands. The symptoms of metastatic disease include bone pain, personality changes, confusion, elevated alkaline phosphatase level, seizures, weakness, weight loss, nausea, vomiting,

26

and palpable lymphadenopathy 4. There are several clinical manifestations of the skeletal and endocrine systems due to lung cancer spread. Some of the endocrine manifestations include Cushing’s syndrome and hypercalcemia. Common clinical symptoms of the skeletal system consist of digital clubbing and hypertrophic pulmonary osteoarthropathy; these symptoms occurs in approximately ten percent of lung cancer cases 4. Clinical presentation and radiographic results are first steps in the differentiation or diagnosis of lung cancer type, i.e. small cell lung cancer (SCLC) or non-small call lung cancer (NSCLC) 9. Small cell lung cancer is recognized by lymphadenopathy or the swelling of the lymph nodes and tumor invasion of the mediastinum 9. A characteristic of small cell lung cancer is the tumor or mass is seen in the hilum in approximately 78% of the cases. Patients with small cell lung cancer can present with paraneoplastic syndromes. Paraneoplastic syndromes are a collection of clinical signs and symptoms resulting from the byproducts of the tumor interrupting normal biological function 4. Some of the syndromes resulting from small cell lung cancer include Lambert-Eaton syndrome (muscle weakness), inappropriate antidiuretic hormone, and ectopic adrenocorticotrophic hormone production 9.

Procedures for Diagnosing Lung Cancer Early detection and treatment is credited with increased survival for early stage lung cancer 13, 28. Early stage lung cancer is defined ICD-9 code as Stage I; which is less than 3 cm and with no evidence that the disease has may has spread outside the lung. There are

27

several early detection technologies providing diagnosis of lung cancer. These include are Computerized Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) Scans 113, 129. Early treatment choices for lung cancer include chemotherapy, surgery, Radiation Therapy and combined modalities 4, 48, 130. In Radiation Therapy, there have been advancements in computer technology allowing the scanning results of CT, MRI and PET to be merged into a three dimensional (3-D) treatment planning system for more precise Radiation Therapy treatments 72. The technological treatment advancement of Intensity Modulated Radiation Therapy can focus the radiation beam into very specific treatment fields that are created or simulated on the treatment planning system by using CT scan and/or by merging of CT and PET scans 72. Literature has shown that staging of cancer patients has vastly improved with the aid of PET 131-133. In many instances, the patient treatment plan has been changed drastically. Cancer patient cases thought to be primary and hence the patient would have received a very aggressive treatment become palliative with less cost, physically, emotionally and economically, to the patient and community at large 131-133. The gold standard for diagnosing lung cancer is with a tissue diagnosis. There are several diagnostic methods in order to obtain a tissue which include 1) sputum cytology, 2) a thoracentesis, 3) excisional biopsy of an accessible node, 4) flexible bronchoscopy with or without transbronchial needle aspiration, 5) transthoracic needle aspiration, 6) video-assisted thoracoscopy, and 6) thoracotomy 4. In order to select the most appropriate test or procedure the physician, e.g. pulmonologist, interventional radiologist,

28

or thoracic surgeon, must make a determination of which lung cancer type is suspected. Patients with suspected early stage non small cell lung cancer who are surgical candidates, commonly have a surgical procedure known as a thoracotomy 4, 9, 54, 59, 125, 134, 135

. A patient can be staged as well as having a tissue diagnosis from this procedure 4, 9.

Sputum cytology involves the collection of at least three samples of sputum; it is noninvasive, but if the results are negative, further testing may be required 4, 9, 135. This technique is recommended if the patient has hemoptysis; sputum cytology is indicated for centrally located tumors in the chest cavity. The specificity for this test is 99% and the sensitivity for central tumors is 71%, peripheral tumors are less than 50% 4, 125, 127. If the patient has pleural effusion (fluid between the lung and the chest cavity); a thoracentesis can be performed. Sampling of the fluid can give an indication of the presence of lung cancer. The sensitivity for this procedure is 80% with a specificity of less than 90%. In the case of palpable lymphadenopathy, a biopsy of an accessible node can be a method to obtain a tissue sample. Sputum cytology, flexible bronchoscopy, and transthoracic needle aspiration are procedures employed when the stage and the cancer type are unclear. Flexible bronchoscopy involves passing a scope along the bronchial tract and taking samples of tissue via bronchial washings and/or biopsies. The sensitivity of this procedure or ability to correctly detect the presence of the disease is 88%. Computerized tomography (CT) of fluoroscopic guidance can be utilized while placing the catheter into the patient’s lungs. The sensitivity and specificity of this test depends upon where the tumor is located or where the tissue sample is taken. The sensitivity for diagnosis of

29

centrally located tumors utilizing flexible bronchoscopy is 88% and the specificity is 90%. The sensitivity for peripheral tumors drops to 60 to 70% with this technique. The procedure of choice for peripheral tumors (sensitivity of 90%) under CT or fluoroscopic guidance is the transthoracic needle aspiration; its’ specificity is 97%. It is indicated in nonsurgical candidates with peripheral tumors when the transbronchial needle aspiration is inconclusive. One drawback or complication of this procedure is a pneumothorax (collapsed lung) in 25 to 30% of the patients undergoing the procedure 4, 9. Videoassisted thoracoscopy is a more recent procedure 4 and is used for small peripheral tumors less than 2 centimeters in diameter, pleural effusion , or pleural tumors. The major advantage of a video-assisted thoracoscopy is it can prevent an unnecessary surgical procedure, i.e. the thoracotomy. Lastly, the surgical procedure recommended for the treatment and the diagnosis of early stage non-small cell lung cancer is a thoracotomy in cases with a clearly resectable tumor 4, 9, 126, 136.

Screening There are many histological types of lung cancer and finding a single biomarker or screening tool is a challenge. Several biomarkers are being evaluated as screening or predictors for lung cancer. An effective screening program for lung cancer is important for early detection of the disease which could increase survival. One of the research projects at the Moffitt Cancer Research Center, has the objective of finding a biomarker that will be used to develop an early screening and detection program for those people at risk for lung

30

cancer 137, 138. Present research includes microscopic examination of sputum sample staining patterns. One of the possible biomarkers is monoclonal antibodies (Mabs). The pattern and stain intensity of the Mabs and varying cell characteristics are being investigated as a possible screening tool. The understanding of tumor biology has increased with the recognition of genetic and protein markers which precede malignancy 137

. Mutations of particular genes contribute to the process of epithelial carcinogenesis.

These mutations modify the control of abnormal cell growth. Heterogeneous nuclear ribonucleoprotein (hnRNP) has been linked as a marker with sputum cytology for early detection of lung carcinogenesis. Data demonstrate that hnRNP is expressed in most lung cancer cases before any morphologic abnormality. Other biological markers that are found in lung tumors include: tumor suppressor genes (p53, Rb, p16, p21), proto-oncogenes (K-ras, c-myc, c-erB-1 and 2, HGF, HER-2), Telomerase (hTERT), hypermethylation and growth factors (GRP/BN, TGF-b, FDGF, PTHrP, IGF-I and II), apoptosis and angiogenesis (Bcl-2, VEGF), and gene amplification (HER-2) 137. These molecular markers can be important markers for pulmonary carcinogenesis, used as an early diagnosis tools, and can be determinants in prognosis of a lung cancer treatment regimen. As shown in the Table 1 (Chart 2) below from Duarte, et. al, 2005, several biological markers are found with greater frequency with respect to the tumor type 139. The molecular marker Rb is found 30% in NSCLC but approximately 100% of the time it is detected in SCLC.

31

Table 1: Molecular Biomarker for Lung Cancer (LC)

Presently, the National Cancer Institute is conducting a large scale clinical trial known as the Prostate, Lung, Colorectal, and Ovarian Screening Trial (PLCO) 140, 141. The objective of this study is to determine the efficacy of screening tools utilized during the trial and evaluate the death or mortality rate associated with that particular cancer under study 142. The major disadvantage with this trial for the early detection of lung cancer is the screening tool, a conventional chest x-ray, does not detect lung neoplasms at early stages 140. Another research study known as the National Lung Screening Trial (NLST) is sponsored by the National Cancer Institute for women and men and women at risk for lung cancer. This particular trial is comparing spiral CT scans and conventional chest x-rays and making a determination which is a more effective screening tool in an effort to reduce death due to lung cancer. Spiral CT scans are effective in the visualization of lung nodules that cannot be seen in conventional chest x-rays; this does creates moral and ethical issue as a spiral CT is proven to detect lung cancer in the early stages as compared to a chest x-ray 143. Presently, the literature does not show that a spiral CT scan or a conventional chest x-ray has not been demonstrated in the literature to 32

reduce the risk of lung cancer mortality 9, 135, 140, 144.

Pathology/Histology There are two major categories of lung cancer; small cell carcinoma and non-small cell carcinoma. NSCLC includes adenocarcinoma, squamous cell carcinoma and large-cell undifferentiated carcinoma 3, 40. Each histological type has its own medical intervention that can include any combination of surgery, radiation therapy, and/or chemotherapy 7, 51, 67. The incidence and survival rates are also different for the lung cancer type. There are many causal factors for lung cancer such as lifestyle, occupational risks, and environmental factors 10. The primary cause of lung cancer is smoking 9, 13, 145-151. Lung cancer remains a major public health problem lacking an early prevention and intervention program 4, 9, 125, 136. As far as Public Health consequences, lung cancer is the most lethal of all cancers as it has the highest mortality rate both among men and women 2, 13 with the average ―5 Year Survival Rate‖ of 13 percent. The four major types of lung cancer are adenocarcinoma, squamous cell carcinoma, large cell, carcinoma, and small cell carcinoma. The four major lung cancer types comprise 95% of a lung cancer cases 72. Adenocarcinoma is a malignant neoplasm; it originates in the epithelial cells of glandular tissue, and forms glandular structures. This particular cancer is very commonly found in the periphery and accounts for 30 40% of all lung cancer types. Squamous cell carcinoma accounts for 20-30% of lung tumors and the origin is usually hilar 7, 152. 95% of all small cell lung cancer is attributed

33

to smoking. SCLC metastasizes early and has a five year survival rate of less than 15%. Large cell carcinomas account for 10-15% of all lung neoplasms and are comprised of undifferentiated or immature cells. Large cell is the most aggressive of the NSCLC as they difficult to diagnose due to its’ undifferentiated nature. These are commonly located in the central portion of the lung. The ICD-O code for lung cancer pathology is classified by the morphology code and the topography code as shown below in Figure 6.

Figure 6: Structure of a Morphology Code Source: http://training.seer.cancer.gov

The topography code identifies the site and the sub-site for the disease of interest. The complete ICD-O code contains ten digits with the first four digits being the topography and the last six digits the morphology identifiers. Figure 7 is an example of the coding scheme for a squamous cell lung cancer. Diagnostic term: Poorly differentiated squamous cell carcinoma, upper lobe of lung C34.1 M-8070/33

Figure 7: Structure of a Complete ICD-O Code Source: http://training.seer.cancer.gov

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The complete site specific topography code for lung cancer is shown in Figure 8 from SEER. Figure 9 displays the anatomy of the lungs with the associated ICD-O code. The codes range from C34.0 as found in the main bronchus to C34.9 NOS (not otherwise specified). ICD-O

TERM

C34.0

Main bronchus

C34.1

Upper lobe, lung

C34.2

Middle lobe, lung (RIGHT LUNG ONLY)

C34.3

Lower lobe, lung

C34.8

Overlapping lesion of lung

C34.9

Lung, NOS

C33.9

Trachea, NOS

Prior to the Second Edition of ICD-O, trachea and lung had the same ICD-O code. With the advent of ICD-O-2, trachea has a separate code (C33.9) from lung (C34._). The ICD-O four-digit subsites of C33.9 through C34.9 are considered part of a single primary site. Since lung is a paired organ, laterality must be coded.

Figure 8: ICD-O-3 Site (Lung) Codes Source: http://training.seer.cancer.gov/ss_module03_lung/unit03_sec01_icdo_codes.html

Figure 9: Lung Anatomy with ICD-O-2/3 Codes Source: http://training.seer.cancer.gov/ss_module03_lung/unit03_sec01_icdo_codes.html

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Staging/Extent of Disease There are several purposes why it is necessary to stage a cancer case. First, the medical professional must assess the extent of the disease adequately 1, 4, 40. Correct ascertainment of the extent of the disease is crucial as the appropriate treatment regimen must be determined 10. A curative or palliative approach in disease management will be based on the stage of lung cancer the patient has. Secondly, staging can one of the indicators of patient’s prognosis and projected survival. Other indicators of prognosis include tumor histology, grade of disease, and patient demographics, e.g. age, gender, race, socioeconomic status, and martial status 55, 139, 153, 154. Finally, with a comprehensive and standardized staging protocol, the exchange of information between the scientific communities can be accomplished. Staging or coding data are use for research and general health care information. The extent of the disease can be classified by the use of a number or coding system with increasing values representative of increasing disease severity. The anatomic coding system allows for analysis of similar cases with comparable characteristics based on disease extent 11. Cancer cases are described by the site of origin called the primary site and how far the cancer has spread from the primary site. Other essential variables include tumor size, the number of tumors (multiplicity), the depth of the tumor invasion, regional or distant tissue extension, regional lymph node involvement, and distant metastases 13. Coding information began on an international level in 1893 for mortality data. The League of Nations’ World Health Organization (WHO) introduced the concept of

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staging a disease in 1929. One of the first descriptions of the extent or the stage of the disease was for carcinoma of the cervix 13. After World War II, the World Health Organization established guidelines for the classification of disease. In 1948 the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD), manual was published; the coding scheme was used to code and tabulate morbidity and mortality data. The American Cancer Society 2, in 1951, developed the first code manual for the classification of tumor morphology. The tumor codes were comprised of the first two numbers being the indicator of the tumor type with a third number representing the behavior of the neoplasm 2, 29. WHO adopted a coding system based on the ACS in 1956 2, 29

. The International Agency for Research on Cancer (IARC) was commissioned by the

World Health Organization to help develop a world wide classification scheme for oncology. The first edition of this manual was in 1976 called the International Classification of Disease for Oncology (ICD-O) 2, 29. There have been several updates and revision for the classification scheme but morphology code uses standardized threeand four- character categories 1, 2, 29. In the United States, the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute routinely gathers data on cancer statistics from designated population based cancer registries 11, 13, 60. SEER has developed a two stage classification system, extent of the disease and a summary stage. Summary staging is based in how the cancer advances or grows and there are five main categories 11. The Commission on Cancer (CoC) of the American College of Surgeons uses the American

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Joint Committee on Cancer (AJCC) staging system. An earlier version of the AJCC classification scheme was first introduced in 1958 by the Union Internationale Contre le Cancer (UICC) 29. In 1959, the American Joint Committee on Cancer Staging and End Results Reporting (AJC) was organized and adopted the UICC coding system 29. The AJC changed their name to The American Joint Committee on Cancer in 1980. This system classifies the tumor in terms of the primary tumor (T), the regional lymph nodes (N), and distant metastasis 155. T is the indication for size and the invasiveness of the primary tumor. It (T) describes the size of the tumor in millimeters or centimeters with the extension of the disease into the adjacent tissue, e.g. mucosa, submucosa, muscularis, subserosa, serosa. T0 indicates there is no tumor; T1 indicates carcinoma in situ and limited to surface cells, and T1-4 reflects increasing tumor size and disease extension 1, 11. The N component is indicative of nodal involvement or no lymph node involvement. An increasing numerical value represents increasing disease extension into the lymph nodes. M is used in the identification of metastatic disease and distant lymph node involvement 1, 11

. Another numeric system commonly in cancer registries used to describe or

classify the extent of the disease is Stage 0 through Stage IV 1, 11. Stage 0 limits the disease to the surface and is also known as cancer in situ, Stage I confines the cancer growth to the tissue of origin and gives evidence of cancer growth, Stage II describes the cancer as limited local spread, Stage III is extensive local and regional spread, and Stage IV is used to classify distant metastasis 29.

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The ICD-O coding system uses a morphology code based on the histology (cell type), behavior code, and grade 1, 11. The behavior indicates if the tumor is malignant, benign, in situ, or if the diagnosis is uncertain. Grading is determined by microscopic examination of the tumor cells 1, 11. The cells are classified Grade I through Grade IV; Grade I, the cells are slightly abnormal and well differentiated, Grade II cells are moderately differentiated and the cells appear more abnormal, Grade III cells are very abnormal and poorly differentiated, and Grade IV are undifferentiated and immature. Immature or primitive cells are undifferentiated and highly abnormal in appearance. If a cell is well differentiated, it can appear like a mature or specialized cell. Table 2 below summarizes the AJCC staging system originally based on clinical data or surgical findings 11.

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Table 2: AJCC TNM Staging System for Lung Tumors American Joint Committee on Cancer (AJCC) TNM Staging System for Lung Tumors Tx

Primary tumor cannot be assessed, or tumor proven by the presence of malignant cells in sputum or bronchial washings but not visualized by imaging or bronchoscopy T0 No evidence of primary tumor Tis Carcinoma in situ T1 Tumor 3 cm or less in greatest dimension, surrounded by lung or visceral pleura, without bronchoscopic evidence of invasion more proximal than the lobar bronchus† i.e., not in the main bronchus T2 Tumor with any of the following features of size or extent: • More than 3 cm in greatest dimension • Involves main bronchus, 2 cm or more distal to the carina • Invades the visceral pleura • Associated with atelectasis or obstructive pneumonitis that extends to the hilar region but does not involve the entire lung T3 Tumor of any size that directly invades any of the following: chest wall (including superior sulcus tumors), diaphragm, mediastinal pleura, parietal pericardium; or tumor in the main bronchus less than 2 cm distal to the carina, but without involvement of the carina; or associated atelectasis or obstructive pneumonitis of the entire lung T4 Tumor of any size that invades any of the following: mediastinum, heart, great vessels, trachea, esophagus, vertebral body, carina; or separate tumor nodules in the same lobe; or tumor with a malignant pleural effusion‡ †The uncommon superficial tumor of any size with its invasive component limited to the bronchial wall, which may extend proximal to the main bronchus, is also classified T1. ‡Most pleural effusions associated with lung cancer are due to tumor. However, in a few patients, multiple cytopathologic examinations of pleural are negative for tumor. In these cases, fluid is not bloody and is not an exudate. Such patients may be further evaluated by videothoracoscopy (VATS) and direct pleural biopsies. When these elements and clinical judgment dictate that the effusion is not related to the tumor, the effusion should be excluded a staging element and the patient should be staged T1, T2, or T3. §M1 includes separate tumor nodule(s) in a different lobe (ipsilateral or contralateral). NX: Regional lymph nodes cannot be assessed N0: No regional lymph node metastasis N1: Metastasis to ipsilateral peribronchial and/or ipsilateral hilar lymph nodes, and intrapulmonary nodes N2: Metastasis to ipsilateral mediastinal and/or subcarinal lymph node(s) N3: Metastasis to contralateral mediastinal, contralateral hilar, ipsilateral or contralateral scalene, or supraclavicular lymph node(s) Stage 0: Carcinoma in situ Stage IA: T1, N0, M0 Stage IB: T2, N0, M0 Stage IIA: T1, N1, M0 Stage IIB: T2, N1, M0, T3, N0, M0 Stage IIIA: T1, N2, M0, T2, N2, M0, T3, N1, M0, T3, N2, M0 Stage IIIB: T4, N0, M0, T4, N1, M0, T4, N2, M0, T1, N3, M0, T2, N3, M0, T3, N3, M0, T4, N3, M0 Stage IV: Any T, any N, M1 Source: American Joint Committee on Cancer (AJCC)

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Lung Cancer Prognosis There are certain factors that affect prognosis, i.e. quality of life, chance of recovery, survival, for a disease. In particular, the prognostic factor of interest for this research is to expand the scientific knowledge concerning gender differences in women and men with lung cancer and survival. The literature has cited several prognostic factors that affect lung cancer survival 49, 55, 139, 154. The prognosis can be dependent upon 1) the stage of lung cancer (the size of the tumor, whether the cancer is confined to the lungs or has spread to other places in the body, i.e. metastized), 2) the histologic type of lung cancer, 3) if there are respiratory symptoms, e.g. coughing, difficulty breathing, and 4) the patient’s general health or well-being 4. Early stage disease (Stage I, Stage II, resectable Stage III) prognostic factors most critical to decreased survival have been shown to include large tumor size and presence of lymph node metastasis, male gender, age greater than 60 years, and having a wedge resection versus a lobectomy or pnumonectomy 55, 61, 156. In advanced stage lung cancer, poor performance status, weight loss, male gender, elevated serum lactate dehydrogenase, and liver/bone metastasis are key prognostic factor for poor survival 17, 61, 157, 158. Clinical research has identified more than 150 risk or prognostic factors according to Blanchon, et. al., 2006 153. Prognostic factors investigated by Blanchon, et. al., 2006’s research included age, gender, socioeconomic status, possibility of occupational origin of the cancer, stage of cancer at time of diagnosis, smoking history (pack-years, duration, discontinuation, date of discontinuation), geographic location, histology, stage, vital

41

status, treatment modality, and performance status at time of diagnosis 153. Performance status was based on the classification as given by the Eastern Cooperative Oncology Group 158, 159, based on a 0 to 4 scale. The final univariate model included age, sex, performance status, histologic types, and stage of disease. Patients younger than 50 years had a greater probability of survival as compared to those greater than 70 years. Men had a decreased survival (Hazard Ratio (HR) = 1.17; 95% CI: 1.05-1.31) versus women. The HR for risk of death increased with increasing stage of disease (Stage IV HR = 3.53; 95% CI 3.05-4.09) and performance status (Performance Status 4 HR = 4.97; 95% CI: 3.836.43) 153. These five predictor variables served as the most important prognostic factors for Blanchon’s research. Other medical research has reported similar individual characteristics (prognostic factors) such as gender, sex, stage of disease, performance status, molecular biologic markers, marital status, smoking status, and psychosocial factors as predictors for lung cancer survival 61, 156, 157, 160, 161; similar to Blanchon et al. (2006). In the article ―The Lung Cancer Database Project at the National Cancer Center, Japan: Study Design, Corresponding Rate and Profiles of Cohort‖, Nakaya, et. al., 2006, reported the importance of having a database with available prognostic factors for lung cancer survival 160. The authors stressed the epidemiologic, psychosocial, and molecular biology data as to improve lung cancer patient outcome by increase treatment efficiency. In this particular study, biologic material was collected and several questioners at baseline and subsequent follow-up intervals 160.

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Epidemiologic studies have shown that there is a causal relationship between smoking and lung cancer 149, 162, 163. Smoking patterns and status serves as a prognostic factor in survival after the diagnosis of lung cancer but the majority of heavy smokers do not develop lung cancer 139. Duarte, et. al. 2005 has suggested that genetic factors affect an individual’s susceptibility to the development of lung cancer. Molecular changes in lung cancer may serve as an indicator (prognostic factor) for survival. Several genetic factors have been investigated, but presently there is no evidence that a single parameter has sufficient treatment efficiency 139, 153. Principle molecular markers primarily found in lung cancer will be expanded upon in the section on Genetic Risk Factors in this chapter. Biologic or molecular markers can be important as prognostic variables but also in the identification of treatments targeting the cancer cell based on the patient’s genetic code for an effective cancer cell kill. This is becoming increasingly important for the treatment of lung cancer and survival

Lung Cancer Survival and Risk Factors Gender Why is gender important as risk factor for lung cancer survival? Although lung cancer mortality has reached epidemic levels for women (an increase of 600 % since the 1950’s), the causal pathways are blurred for women 40. The exact etiology of a woman’s susceptibility (reported in the literature as different from men ) to lung cancer, still not resolved 49. One potential reason for the current deficit of knowledge for gender

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differences in the etiology and subsequent susceptibility is that the treatment patterns for lung cancer are based on research done on men. Previously, the association between being a woman and the risk of lung cancer was considered negligible by the medical community as referenced by published reports by the US Surgeon General 24, 76 . But as behavior and other temporal changes, such as cigarette smoking have occurred over the past several decades, the incidence of lung cancer has increased with a resultant increase in mortality 2, 29. Women historically have been excluded from clinical trails or if included, the data was not analyzed 18. As one result of this disparity of being excluded from research studies, women may be at a greater risk of lung cancer than men at the same level of smoking but the evidence in the literature has be conflicting and is limited 22

. A report of the Surgeon General 24, 76 on women and smoking did not reach any

conclusions concerning what role gender difference may play in the development of lung cancer (US Department of Health, and Human Services, 2001). Hypotheses have been developed based on possible response to carcinogens and hormonal related differences in women as compared to men (Ryberg et al., 1994) but conflict in the literature remains 27, 40, 45, 50, 107, 164

.

Lung cancer incidence and mortality rates are higher in men as compared to women 3, 10. The fact that women have a lower prevalence of smoking may account for this difference. The primary cause of lung cancer in women and men is due to smoking tobacco products, in particular cigarettes 23, 75, 165, 166. In 2006, it is estimated lung cancer will account for 30 percent of all cancer deaths in the United States. Among men, lung

44

cancer incidence and mortality have been declining since the early 1980s and 1990s. The peak death rate in men in the 1990s coincided with a lag period of approximately 25 years after the highest per capita cigarette consumption. Women started smoking approximately 20 years after men, lung cancer incidence rates did not begin to fall in women until 1998. For the first time in 1995, mortality rates in women have stabilized, after increasing for several decades. There has been an increase of 600% in mortality for women with lung cancer since 1930 28, 40, 60. Lung cancer has overtaken breast cancer as the number one cause of cancer related deaths of women in the United States. In the next several articles cited in this chapter, epidemiological studies were designed to quantify the differences in lung cancer risk between men versus women 8, 15, 17, 27, 167

. In the paper, "Lung cancer in women compared with men: stage, treatment, and

survival", Ouellette et al., 1998, conducted a retrospective cohort study, to test the hypothesis of a survival difference among men and women with lung cancer 8. The target population consisted of 104 women and 104 men with incident cases of lung cancer diagnosed between March 1998 and June 1990 at a university hospital in Montreal, Canada. The authors concluded there was no difference in mean age of lung cancer diagnosis for females (60.97+ 10.89 years of age) and for male patients (61.49 +10.29 years of age). There was a statistically significant difference in the distribution of the different histologic types of lung cancer between men and women (p = 0.028). When Ouellette et. al. stratified on lung cancer stage, the stage of the disease positively influenced survival between the women and men 8. After adjustment, women appeared

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to live 12 months longer than men at any stage and a statistically significant survival advantage in women was found (p = 0.02) 167. The authors (Ouellette et. al. ) found that women did received less surgical interventions than men; although not statistically significant 8. Twenty-four women received chemotherapy compared with fourteen men, although this was not found to be statistically significant according to the authors. A limitation in this study could be due to the small number of lung cancer cases contributing to the non-significant finding (decreased power). Ouellette et al., 1998 reported men and women received similar treatments for their disease in this study. This differs from studies on coronary artery disease in which it was thought that physicians may pursue less aggressive management in women as noted by Steingart et al., 1991 8. Unlike Ouellette, et. al., 1998, Aitakov et. al., 1998, found that more men in Russia underwent surgery with a ratio of men to women of 7.4:1.0 167. Ouellette et al. did not find such a disparity; the ratio was 1.17:1.0 men to women, and noted that is probably reflected the tendency to offer similar treatments to both sexes in the Western world. A population-based study by Radzikowska et al., (2002), investigated demographic factors (gender, age, and smoking) and factors connected with the disease (histology, performance status, stage, treatment and survival) for lung cancer patients. The target population was comprised of members of community-based cancer registries. Approximately 20,561 lung cancer cases from all parts of Poland, from 1995 to 1998, were registered with the National Tuberculosis and Lung Diseases Research Institute

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(NTLDRI), as well as being registered with the Polish Cancer Register. From this population, 2,875 women and 17,686 men were selected 15. It was determined that women developed lung cancer at a younger age, were more likely to be lifetime nonsmokers, consumed fewer cigarettes per day and smoked for a short period of time 15. The authors commented that all those factors suggested that women are more susceptible to the carcinogenic compounds of cigarette smoke and environmental noxious conditions that possibly damaged the genetic distribution for the population 15. Women were found to be more likely to have adenocarcinoma and SCLC as compared to men. Squamous cancer was the predominant type of lung cancer among men, and less than ten percent of men had adenocarcinoma. Different patterns of histological types of lung cancer were observed in Poland as compared to the USA, China or Denmark, where overrepresentation of adenocarcinoma has been noted. The distribution of main histological types of lung cancer in Poland was similar to that described in Finland and Scotland. The most important prognostic factors for lung cancer patients were performance status, clinical stage of the disease and surgical treatment. The authors did not evaluate different treatment modalities and the effect on survival. Radzikowska et al., found that females with lung cancer had a survival benefit compared with males, taking into account age, histology, performance status, extension of the disease and treatment. This overall survival advantage of women was described first in data based on Danish Register information. Although Radzikowska, et al., found an increased survival based on gender the other researchers found the opposite. Kirsh et al., in their 1982 article ―Carcinoma of

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the Lung in Women‖ found that the survival among women in a younger age group was significantly lower than for both groups of women in the older age group (p = 0.0335) and men in the younger age group (p = 0.0033). This was believed to be due to the higher incidence of both Stage III disease and adenocarcinoma in younger women. Visbal et al. (2004) "Gender differences in non-small-cell lung cancer survival: an analysis of 4,618 patients diagnosed between 1997 and 2002 evaluated the magnitude of the gender effect on non-small-cell lung cancer (NSCLC) survival across disease stage, tumor histology, and therapies 17. The target population of 4,618 newly diagnosed NSCLC were patients at the Mayo Clinic in Olmsted County, Minnesota between 1997 and 2002. There were 2,724 men (59%) and 1,894 women (41%), with a median age at diagnosis of 68 years in men and 66 in women (p < 0.01). Women were diagnosed on average two years younger than men 17. As compared to men, women began smoking at a later age, smoked less cigarettes per day and fewer years. More men smoked and were heavier smokers than women17. Adenocarcinoma was the common subtype in both genders; 59.5% of the women and 48.2% of men. The difference between women and men with adenocarcinoma was significant with a p-value of <0.001. For the other histological types (squamous, unclassified NSCLC, large cell, adenosquamous) of lung cancer the difference was not significant between men and women with. The estimated relative survival in men was 51% (95% CI: 49%, 53%) at one year and 15% (95% CI: 12%, 17%) at five years. The estimated one year relative survival in women was 60% (95% CI: 58%, 62%) and 19% (95% CI: 16%, 22%) at five years 17. Men were at a

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significantly increased risk of mortality compared to women following a diagnosis of NSCLC (adjusted relative risk: 1.20, 95% CI: 1.11, 1.30), particularly for patients with stage III/IV disease or adenocarcinoma. Male gender was found an independent unfavorable prognostic indicator for NSCLC survival 17. This particular study by Visbal et. al. was of interest to this research as Visbal studied treatment types, stage and histology of the disease; as noted previously reports on stage, grade, histology, and treatment are limited. Some of the weaknesses of this study included the lack of interaction terms in the model as without evaluating interaction any significant effects could be masked. The 2005 article by Ringer et al., ―Influences of Sex on Lung Cancer Histology, Stage, and Survival in a Midwestern Untied States Tumor Registry‖ 27 attempted to expand the current knowledge of gender differences in men and women with lung cancer and survival (N= 2618). Squamous cell carcinoma was the predominant histologic type of lung cancer for men; women had an increased likelihood of having adenocarcinoma or small cell lung cancer. There was no statistically significant difference for men with large call carcinoma versus women with large call carcinoma. The stage of disease at diagnosis for men as compared to women was not significant27. Differences in survival were demonstrated between the lung cancer types during the cutpoints of 3, 4, and 5 years. A very pronounced survival difference existed between men and women for stage IV squamous cell carcinoma (274 mean days for men versus 153 mean days for women, p = 0.005). Women diagnosed with squamous cell carcinoma stage II again had

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decreased survival versus men (636 mean days for men, 379 mean days for women, p = 0.043). This study demonstrated the important effect histologic type and stage at diagnosis plays in overall survival when comparing gender differences 27. It has been hypothesized that women are more susceptible to tobacco products as compared to men.

Tobacco The number one cause of lung cancer, approximately 80% of lung cancer cases can be attributed to smoking tobacco products, and the subsequent decrease in survival for lung cancer cases is due to tobacco smoke 10, 168. The etiology of lung cancer is multicausal with a complex pathway of development that includes carcinogen exposure, metabolism, and genetics 48. Tobacco smoke is recognized as the chief risk factor for lung cancer 25, 67, 92, 169. It has been estimated by the International Agency for Research on Cancer (IARC) to contain at least 80 known mutagens and carcinogens, e.g. polycyclic aromatic hydrocarbons (PAH’s), N-nitro amines, and aromatic amines. Yach and Wipfli stress the importance of tobacco-control efforts as tobacco kills five million people annually with an estimated increase of 100% (10 million) by mid 2020 170. Today the population attributable risk percent (PARP) for men is approximately 90% and for women the PARP is approximately 80% 10. The effects of tobacco smoking were demonstrated in the 1950’s with an epidemic rise of lung cancer in US males. The increase in lung cancer rates were first attributed to factors other than tobacco smoke such as atmospheric pollution 170. As the rise in lung cancer rates and the increase in

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mortality rates for lung cancer, clinical evidence and case series were reported in the literature during the 1930’s for a suspected link between tobacco smoke and an increased risk of lung cancer. In 1938, Raymond Pearl, M.D. from Johns Hopkins University reported that smokers do not live as long as nonsmokers 170. There were two classic epidemiologic studies that demonstrated a strong association between the risk of lung cancer and smoking by 1) Sir Richard Doll and 2) Sir Bradford Hill 10. Sir Richard Doll conducted a case control study in 1947 and compared hospitalized patients with and without lung cancer. He collected information on smoking history and found a greater than 20 percent increased risk for lung cancer. Zang and Wynder found that women were at an increased risk (1.2 to 1.7 times) for lung cancer than men independent of tobacco smoking level 171 with an associated decrease in survival. Passive smoke, also known as second hand smoke or environmental tobacco smoke (ETS) has been shown to increase the risk of lung cancer 10, 172. Stockwell et al. (1992) demonstrated an increased risk, OR = 2.4; 95%CI = 1.1-5.3, for women with 40 or more smoke-years of household (ETS) exposure as an adult. The authors also found a statistically significant association with women exposed to ETS during childhood or during adolescence for 22 smoke-years with an OR = 2.4; 95%CI = 1.1-5.4 173. Investigators for the International Early Lung Cancer Action Program found that women have an increase susceptibility to lung cancer as compared to men, OR = 1.9; 95%CI = 1.5-2.5 yet women had a decrease hazard ratio for survival versus men, HR = 0.48; 95%CI= 0.25-0.89 174. The study population was comprised of 1202 men and women

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from New York City undergoing a baseline screening at Weill Medical College of Cornell University 174.

Race and Ethnicity Worldwide statistics for lung cancer obtained from the International Agency for Research on Cancer, IARC, and GLOBOCAN 2002 demonstrate that lung cancer incidence and mortality rates are less in women than men 5. The decreased rates for women are not dependent upon ethnicity and race as reported by IARC. Some of the variation in the incidence rates may be attributed to the quality of the data collected by the cancer registries and diagnostic methods employed. Gadgeel and Kalemkerian (2005) reported that race is not a biologically relevant parameter but racial differences in lung cancer characteristics and outcomes have been reported 21. Blacks consistently have higher rates of lung cancer incidence as compared to whites. African-Americans in the United States have the greatest incidence of lung cancer (8.5% risk of lung cancer diagnosis); they also have the highest lung cancer mortality rates (7.6% risk of death form lung cancer) 10. Racial differences in smoking habit sand SES, have been associated with an increased risk of lung cancer 21. Willsie and Foreman (2006) in the article ―Disparities in Lung Cancer: Focus on Asian Americans and Pacific Islanders, American Indians and Alaska Natives, and Hispanics and Latinos‖ evaluated lung cancer incidence and mortality based on racial and ethnic groups 175. The authors noted racial and ethnic differences in smoking habits,

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presentation, stage at diagnosis, metabolism of nicotine, treatment received, and outcomes will impact lung cancer survival and health care over the next several decades in the United States 175. The identification of ethnic groups is an important aspect of lung cancer research for possible gender differences based on ethnicity and lung cancer survival. The Multiethnic/ Minority Cohort study was established to study diet and cancer in the United States 176. The data can be and is utilized to evaluate lung cancer patterns of incidence and mortality and the effect gender, race, and ethnicity play. The cohort consisted of 215, 251 living in California (primarily in Los Angeles County) and Hawaii with the cohort consisting of 16.3% African-American, 22.0% Latino, 26.4% Japanese-American, 6.5% Native Hawaiian, 22.9% White, and 5.8% of other ancestry. African American had the highest rate of smoking, 28.5%, followed by Native Hawaiian at 20.1%. The lowest groups of smokers were Japanese Americans, 15.5%, and whites, 15.9%. Both females and male African American and Native Hawaiian females had the highest prevalence of smoking as compared to male and female Japanese Americans and Latinas. Lung cancer incidence was 54.0% lower for Japanese American men (p-value < 0.001) and 71.0% lower for Latina women (p-value < 0.001) as compared to African Americans 176.

Genetics Prior to 1970, scientific evidence about the etiology of lung cancer was unavailable. A pathway of understanding the complex route of lung cancer development

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with the consequent effects of gender differences and survival became accessible with advances in molecular genetics 83. Cancer is a complex process which involves an initial damage of the genetic material (DNA, RNA) of a cell; this leads to a mutation or change in the chromosomes19, 67, 69, 71, 177-180. The mutations can be inherited (germ cells) or may occur ―somatically‖ in the stem cells; resulting in clone cells of the original cell that may become cancerous (malignant and uncontrolled cell growth). Although identification of biologic materials provides evidence of genetic susceptibility; inter-individual variation can modify the effects of carcinogenic exposures and the resultant effects. For an example, the majority of long term smokers will not develop lung cancer. A predictive model for lung cancer genetic susceptibility among smokers was developed by Bach et al. (2003) 92, 117, 118. Utilizing data from the CARET trial, 18, 172 individuals, the statistical model predicted only a quarter of the lung cancer cases based on genetics predisposition; individual variation of metabolism, DNA repair, cell cycle, inflammation and microenvironment would be a possible explanation for the inability to calculate an accurate and precise model of behavior 92. Several genetic and epigenetic mutations of tumor suppressor genes have been observed in lung cancer 48. Thomas et al. (2006) noted in 95% of small cell lung cancers and 40 to 70% of NSCLC, the most frequently encountered genetic alteration was p53. The p53 affects the biological pathway in G1 and G2 cell cycle regulation; p53 stops the cell division that occurs when there is damage to the DNA 48. The p53 mutation leads the formation of DNA adducts (abnormal piece of DNA bonded to a cancer causing agent) as

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a response to the effects of smoking; this mutation inhibits the normal cell repair and is involved in carcinogenesis. Women have been found to have increased DNA adducts per pack years versus men. K-ras (a proto-oncogene) is another biomarker and forms DNA adducts when damaged; women are three times more likely than men to have the K-ras mutation; this mutation is associated with adenocarcinoma.

Family History Another risk factor impacting lung cancer survival can be associated with family history or familial aggregation 10, 172. Familial aggregation can serve as a surrogate (indirect measure) for lung cancer etiology and resultant survival rates based on genetic predisposition 181. Etzel et al. (2003) examined risks for smoking related cancers for relatives of lung cancer patients 169. Siblings were found to have a statistically significant association, RR = 1.85; p-value = 0.003, of lung cancer risks as well as an increased risk for smoking-related cancers, RR = 1.29; p-value = 0.01 169. When stratification on age of disease onset was done, there was no association between familial aggregation and lung cancer risk for ages less than 55 years. The authors did find evidence of familial aggregation, RR = 1.71; p-value < 0.001, for lung cancer risk between relatives of lateonset cases of lung cancer. Schwartz et al. (1999) found evidence that common susceptibility genes may increase the risk for lung cancer among relatives of nonsmoking lung cancer cases 182. The study population was obtained from families of nonsmoking lung cancer cases (257 population-based) and nonsmoking controls (277 population-

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based), residing in metropolitan Detroit. The first-degree nonsmoking lung cancer cases relatives were (OR = 1.5; 95% CI = 1.02-2.27) at increased risk for cancer of the digestive system after adjustment for each relative's gender, race, age, and smoking status. There was an elevated OR of 1.12 for an increased risk for lung cancer for first degree relatives but the findings were not statistically significant, i.e. 95% CI = 0.65-1.93 182

.

Genetics and the Environment A powerful design to disentangle the interplay between genetics and environmental influences in the studies of human disease incidence, mortality, and survival can be accomplished by the use of twin studies 10, 172. Twin studies serve to separate the affects of an individual’s biological makeup and environmental influences. Identical twins (monozygotic (MZ)) develop from fission of single fertilized egg and have inherited identical genetic material; fraternal (dizygotic (DZ)) twins derive from two distinct fertilized eggs meaning they have the same genetic makeup comparable to siblings 10, 172. Genetic effects would be determined significant if there was concordance for cancer among MZ twins as compared to DZ twins (on average share 50% of their separated genes). Environmental factors would be the determining factor for increased lung cancer risk if the concordance was similar for both types of twins 183. Lichtenstein et al, (2000) combined data from three different national twin and cancer registries (44,788 pairs of twins from Swedish, Danish, and Finnish twin registries) 183. The

56

authors found that there were statistically significant risks associated with colon and breast cancer, lung cancer was not. This implies the inherited genetic factors do not make an individual susceptible to lung cancer and survival but environmental factors play the major role 183. Braun et al. (1995) concluded that genetic susceptibility had influence on lung cancer mortality 184 in men. There was an excess risk of lung cancer mortality for dizygotic twin pairs (DZ SMR = 2.2; 95%CI = 1.3-3.4) but the risk was not statistically significant for monozygotic twin pairs (MZ SMR = 2.1; 95%CI = 1.0-3.7). This suggests a predisposition for lung cancer in males.

Geographic Variation Survival rates based on lung cancer incidence and mortality cluster in geographic regions that have a high prevalence of smoking 10. Devesa et al. (1999) examined data from the IARC cancer registries of morphology specific lung cancer. Squamous cell carcinoma had declined by 30% in North America 30. Rates in the Nordic countries, which varied by 2-fold from a high in Denmark to a low in Sweden, still were generally lower than in other parts of Europe, where the rate was highest in the Netherlands 30. The lung cancer incidence rates among males varied by 4-fold: 83.6 among U.S. Blacks to 21.1 in Sweden. Among females, recent rates varied by almost 8-fold, with the highest among U.S. Blacks (35.8) and the lowest in Spain (4.6). Incidence rates among females paralleled that in males, with the exception of Switzerland. Rates everywhere were

57

higher among males than females. Male to female rate ratios varied from less than 2 in Iceland, U.S. Whites, Canada, Denmark and Sweden to more than 6 in Slovenia, Italy, and France and more than 10 in Spain. Developing countries demonstrate a higher ratio of lung cancer incidence and mortality for men versus women. As shown below in Table 3, developing countries, have a higher ratio of male and female lung cancer incidence and mortality rates.

Table 3: Incidence and Mortality Rates Incidence and Mortality Rates, Crude and Age-Standardized (World) rates, per 100,000 Incidence Mortality Crude Crude Country/Region Cases Rate ASR(W) Deaths Rate ASR(W) World 1352132 43.5 47.6 1178918 37.8 41.5 More developed regions 676681 114.7 72 584979 99.2 61.2 Less developed regions 672221 26.7 35.3 591162 23.4 31.2 Females World 386891 12.6 12.1 330786 10.7 10.3 More developed regions 194731 31.7 17.1 161472 26.3 13.6 Less developed regions 191192 7.8 9.4 168481 6.8 8.3 Males World 965241 30.9 35.5 848132 27.1 31.2 More developed regions 481950 83 54.9 423507 72.9 47.6 Less developed regions 481029 18.9 25.9 422681 16.6 22.9 Source: GLOBOCAN 2002, IARC

In the United States, Kentucky had the highest incidence of lung cancer, 40 per 100,000, and lung cancer mortality, 115 per 100,000 for the years 1997-2001 for males

58

10

. Utah has the lowest incidence and mortality rates for lung cancer for males and

females based on data from the American Cancer Society.

Alcohol Another risk factor associated gender differences and lung cancer survival is alcohol 10. The causal relationship between lung cancer and alcohol is complicated and remains controversial 10. Confounding by smoking is a major consideration and tobacco smoking commonly exists in setting where alcohol is consumed 185. Nine studies were identified by Wakai et al. (2007) and in the authors’ examination of alcohol and lung cancer; they found only five of the studies adjusted for smoking 185. This meta-analysis concluded that methodological issues explain the elevated risks in the studies of alcohol as misclassification errors based of smoking status were common in the nine studies. Prescott et al. (1999) found a protective association between lung cancer risk and wine drinking 186. This was based on the results of three prospective cohort studies in Denmark. Men who consumed more than 13 glasses of wine per week had an RR of 0.78; 95%CI 0.63-0.97 compared to nondrinkers of wine. The RR’s were elevated and statistically significant for beer drinkers (RR = 1.36; 95%CI 1.02 -1.82) and ―spirits‖ (more than 41 drinks per week RR = 1.57; 95%CI 1.06-2.33). The study made the determination that the type of alcohol consumed impacted the association between lung cancer and alcohol after adjustment for smoking status 186.

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Diet and Micronutrients Scientific evidence from the literature exists pointing to dietary factors as protectors against lung cancer induction 83, 187, 188. This risk factor, diet, may prove to increase survival as it may serve as a protector against lung cancer for men and women. Some of the dietary factors include fruits, vegetables, carotenoids, vitamin C, phenols, flavones, vitamin E, selenium, isothiocyanates, folate, fat, and alcohol 10. The article by Smith-Warner et al., 2003, ―Fruits, Vegetables and Lung Cancer: A Pooled Analysis of Cohort Studies‖ found that after controlling for smoking, there was a sixteen to twentythree percent reduction in lung cancer risk (RR = 0.77; 95% CI 0.67-0.87; p-value (test for trend) =0.001) for men and women that had an increased consumption of vegetables and fruits versus study participants that had a limited intake of fruits and vegetables. Table 4 (Table 1 from Smith-Warner et al. (2003)), lists the prospective cohort studies used in the pooled analysis used for the research. The American Institute for Cancer Research presented a summary of seventeen case-control and seven cohort studies and concluded evidence existed that with an increased intake of fruits, vegetables, and in particular dark, leafy, green vegetables, lung cancer risk was decreased 189. Brennan et al., 2005 studied whether cruciferous vegetables were protective against lung cancer in a case-control study 190. Cruciferous vegetables contain isothiocyanates, non-nutrient compounds found to be effective inhibitors of tumorigenesis 10 . The authors found that weekly consumption of cruciferous vegetables decreased lung cancer (OR = 0.78; 95% CI 0.64-0.96) as

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compared to men and women that consumed cruciferous vegetables less than monthly 190. One of the weaknesses of the study by Brennan et al., was there was no identification or analysis by gender and the possible gender effect of cruciferous vegetables consumption on lung cancer. Insufficient adjustment for smoking has been examined as a possible residual confounder 191 to explain the protective effects demonstrated by an increased intake of fruits and vegetables for a decreased lung cancer risk and subsequent increase in survival. The results of the research done by Skuladottir et al., 2004, found that there was an inverse relationship between an increased intake of fruits, vegetables, and lung cancer risk even after the influences of smoking as a confounder analyzed via stratified analysis. Men and women in the highest quartile of intake of fruits and vegetables demonstrated a thirty-five percent lower risk of lung cancer as compared to individuals in the lowest quartile of fruits and vegetable intake 191. The lung cancer risk and fruits and vegetable intake association was decreased when stepwise adjustment for smoking status, duration of smoking, and the number of cigarettes smoked per day was done but the relationship remained statistically significant for study participants that had the highest intake of all plant food (fully-adjusted rate ratio = 0.65; 95% CI 0.46-0.94) 191. Dietary carotenoids have been identified as the possible micronutrients in fruits and vegetables that may decrease lung cancer risk 84, 192-195. When this effect was tested utilizing clinical trials with high doses of carotenoids, in particular beta-carotene, a reduction in lung cancer risk was not demonstrated 196, 197. One particular clinical trial,

61

the Alpha-Tocopherol, Beta-Carotene Cancer Prevention 198 Trial, was stopped early due to the unexpected result of a statistically significant increase of lung cancer after receiving beta-carotene as compared to participants receiving the placebo 197, 198. Stram et al., 2002 suggested that biases introduced from the method of smoking assessment resulted in the failure of three prospective beta-carotene clinical trials: CARET, ATBC, and the PHS 199-202. Gender specific lung cancer survival may be influence by a dietary factor, fat. There has been extensive research into the association between dietary fat and lung cancer risk 203-211. Alavanja et al. (2001), Goodman, et al. (1992), and De Stefani et al. (1997) (study restricted to men) reported an elevated risk of lung cancer with an increased consumption of fat 204, 206, 212. A non-statistically significant association was demonstrated by Swanson et al., 1997210 for intake of red meats and increased lung cancer risk after adjusting for confounders. Conflicting results in the literature as cited in this chapter could be suggestive of inaccurate reporting and possible recall bias 10.

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Table 4: Lung Cancer and Food Intake Cohort Studies

Obesity and Body Mass Index (BMI) The major controversies concerning the exposure disease relationship between gender, obesity, BMI, lung cancer risk and lung cancer survival exist in the literature. There are conflicting opinions in the scientific community concerning the association 40, 213

of low BMI and elevated lung cancer risk. In case-control studies, the literature 26, 108,

214-219

cite that low body mass index or ―leanness‖ is associated with the increased risk of

lung cancer. The scientific unit for BMI is 1 kg/m2 220. The three levels of exposure are: low BMI (> 25 kg/m2), normal or the reference group BMI (> 21.9 kg/m2to <25 kg/m2), and the high BMI group (> 25 kg/m2). As noted in a report from the January-February 2005 FDA Consumer Report, the average or median BMI has increased from 25 kg/m2 in 1960 to 28 kg/m2 in 2002 for the general US population. BMI can serve as a proxy

63

measure for overweight and obesity. It is widely cited in the literature 220 that a high BMI (> 25 kg/m2) is associated with an increased risk to hypertension, diabetes mellitus, ischemic heart disease and in particular most cancers. A contradiction exists with lung cancer and low BMI (> 25 kg/m2) 40, 218, 219 where there is an inverse relationship. As pointed out previously, the evidence based on BMI and cancer is blurred due to the progression of lung cancer and that effect on mortality 106, 220. Weight loss may occur (which affects BMI) due to the cancer process prior to the disease being diagnosed adding to the difficulty associated with making a definitive causal inference in the BMI/Lung Cancer association 5, 106, 108, 220, 221. Some of the limitations of previous obesity, BMI, and lung cancer risk study designs may have served to mask a true association. Kanashiki 218 mentioned in his article that the previous case-control studies investigating the relationship between low BMI and lung cancer were based on participants with symptomatic lung cancer. This may have caused a misinterpretation of the relationship between the exposure and the disease because weight loss may be a sign or clinical symptom of the disease, lung cancer. Kanashiki 218 further reported that there is a statistically significant association between low BMI and lung cancer for men; women did not demonstrate a statistically significant association between low BMI and lung cancer (increased lung cancer risk). Previous cohort studies 108, 219, 222 such as Kabat and Wynder used self-reported body size during data collection. Using a method of self-reported body size has been noted to be problematic in the literature 223; as overestimates of height and underestimates of weight

64

can be reported with this method. As noted by Henley 213, another issue with all previous prospective studies is that the numbers were not large enough to exclude those that were smokers or that have preexisting diseases that may reduce body weight. This may have resulted in a spurious association between low BMI and lung cancer. The main effect of BMI and lung cancer has been shown by Rauscher 26 to have an increased Odds Ratio of 1.33 (95% CI 1.13, 1.57) in matched analysis for men and women with high BMI and being a non-smoker. This conflicts with other studies that associate a low BMI with an increase in the risk of cancer as compared to a normal BMI group. These conflicting results serve as an example for the need for the additional clarification that the proposed research study will provide. Biologic plausibility defined by Gordis 224 as a consistency of the epidemiologic findings with existing biologic knowledge. Therefore, without biologic plausibility, interpreting the data or making a definitive statement about the association between the exposure and the disease becomes problematic. In the case of suspect biologic plausibility, Gordis 224 suggests that the requirements for the sample size and the significance of any differences that may be observed may have to be escalated, e.g. increase the sample size to decrease the variability in the sample 155. Hennekens 73 states that biologic plausibility is a causal criterion for an association and that a known biologically plausible mechanism enhances the cause and effect relationship. From the literature review in the section above, the disease process in lung cancer has been shown to influence BMI levels or visa versa. Changes in the association between the exposure and the disease, for example, could be a result of changes in

65

physiology during the preclinical phase of the disease; which would provide a biologically plausible mechanism. The ―exact‖ biological mechanism of how lung cancer changes BMI or how BMI influences lung cancer has not been clearly established.

Occupation Several occupational exposures are known carcinogens and have been classified by IARC (an international agency) and the Occupational Safety and Health Administration (OSHA) in conjunction with the National Institute of Safety and Health (a US based agency) 225. The list of substances considered by NIOSH include arsenic and inorganic arsenic compounds, dintrotoluenes, beryllium and beryllium compounds, cadmium compounds, nickel compounds, and crystalline forms of silica 225. Diesel exhaust, coal tar pitch volatiles, coke oven emissions, and environmental tobacco smoke are other substances of variable chemical composition and are considered carcinogens by NIOSH. Other occupational risk factors (agents) include radon, vinyl chloride, polycyclic aromatic compounds, asbestos, and bischoloromethyl ether 10. Epidemiologic studies estimate a range of attributable risk percent associated with lung cancer and occupational exposure of 9% to 15% 10. Hessing and Hartung explored the excessive rates of respiratory cancers for European underground metal miners in 1879 226. Radford and Renard (1984) examined the increased dose-response relationship for radiation and lung cancer 227. The expected death rate for nonsmoking miners with lung cancer was 1.8 but the observed mortality rate for nonsmoking miners due to lung cancer was 18 227.

66

Deposits of uranium and radium and the subsequent by-products of radioactive decay (radon) were determined to be the causative agent for the development of lung cancer for miners. Other occupational investigational studies included Doll (1952) who demonstrated an increased risk of lung cancer for gas workers and Morgan (1992) reported that mortality from lung cancer had a standardized mortality ratio SMR of 1.65 226

. Hinds et al. (1985) determined risk factors for lung cancer based on excessive

relative risks for a number of occupational groups exposed to coal and tar pitch, diesel fuel and exhaust, arsenic, chromium, asbestos, nickel, and beryllium 228.

Hormones Sex differences in susceptibility and survival have been attributed to estrogen as a lung cancer risk factor and prognostic factor 229. Gender specific estrogen receptor (ER) expression may offer a biologically plausible influence in female lung carcinogenesis 230. Schwartz et al. (2005) conducted a study of lung cancer tissue samples from two population based, case-control studies (214 women and 64 men) 229. Normal lung tissue was obtained for comparison from subject during autopsy that did not have lung cancer. The association between the ER receptor status, subject characteristics, and survival were analyzed. The lung tissue was tested for the presence of nuclear estrogen receptor (ER)-alpha and ER beta with immunohistochemistry. Lung tissue sample for tumor and normal tissue did not stain positive for ER. Nuclear ER receptors were found in 61% of the lung tumor samples (70% of the men and 58% of the

67

women) and in 20% of normal tissue. Females were less likely to have positive ER tumors than males (OR = 0.54; 95%CI = 0.27-1.08). When the analysis was stratified on histologic type, women with adenocarcinoma were less likely to have positive ER tumors than males ((OR = 0.40; 95%CI = 0.18-0.89). Han et al. (2005) found that the gender specific estrogen receptor  (Er) may offer a plausible explanation that inter-individual difference in Er expression (present in the lung) impact carcinogen metabolism and mutation. The research was based on genome studies of genes (CYP1A1, CYP1B1) key in carcinogen metabolism; those genes were the most responsive to cigarette smoke extract (CSE) in normal bronchial epithelial (NHBE) cells 230.

Socioeconomic Status The risk of lung cancer and socioeconomic status patterns can be dependent upon a country’s industrialization. In Canada, Mao et al. (2001) reported males with a lower socioeconomic status had an increased risk of lung cancer as compared to individuals at a higher SES level 231. Females did not show an association between lung cancer risk and SES after adjustment for occupation, education level, income, and social class was made 10

. Singh et al. (2002) reported on changing US area socioeconomic patterns for lung

cancer mortality for the years 1950 through 1998 232. Temporal changes in the distribution of lung cancer mortality were shown for women in all age groups with a 7 times increased risk between 1950 and 1998 with an overall higher mortality of women 68

with lung cancer in higher socioeconomic groups. Lung cancer mortality for men (25-64 years) was 56% (95%CI = 49%-64%) higher in the lowest socioeconomic groups 232. The authors concluded that lung cancer mortality risk based on socioeconomics reversed for males from 1950 to 1998 with women demonstrating an increased risk for lung cancer mortality independent of socioeconomic status 232.

Environment Environmental factors play a distinct role in lung cancer etiology and survival patterns. Passive or environmental tobacco smoke (ETS) and occupational exposures are risk factors for lung cancer and causal associations have been established. Veneis et al. (2007) investigated ETS and traffic related air pollution 233. Attributable risk percents for the proportion of lung cancer cases of never smokers and former smokers were 16 to 24%. The authors concluded that a reduction in air pollution levels, as measured by nitrogen dioxide (NO2) levels less than 30 g/m3, would prevent 5 to 7 % of all lung cancer cases 233. Indoor air pollutants have been studied as risk factors for lung cancer in developed and developing countries 234. In a recent article by Ramanakumar et al. (1997) the risk of lung cancer and residential heating and cooking fuels was assessed for a North American population. The odds ratio for women as compared to men exposed to both traditional cooking and heating sources was 2.5; 95% CI = 1.5-3.6. Oriental women have been shown to be at increased risk for lung cancer, in particular adenocarcinoma, which is attributed to prolonged and concentrated exposures

69

to cooking and heating sources 172, 234.

Diseases Associated with Lung Cancer An individual’s previous history of respiratory disease has been shown to modify the risk of lung cancer incidence and mortality 10, 235. Cigarette smoking and chronic respiratory diseases play a key role is carcinogenesis due to a continued cycle of injury and repair. Schabath et al. (2005) compared the medical histories of 1,375 health controls and 1,553 lung cancer cases in a case control study (1995 through 2003) with a focus on respiratory diseases (asthma, emphysema, bronchitis, hay fever, pneumonia, and TB). Two biologically relevant biomarkers for lung cancer, polymorphic genes (matrix metalloproteinase-1 and myeloperoxidase) were also assessed. Those with emphysema had an elevated for the risk of lung cancer (OR = 2.87; 95%CI = 2.20-3.76). Individuals’ positive for the adverse genotype had a significantly higher risk of lung cancer; OR metalloproteinase-1 +

= 4.98; 95%CI = 2.94-8.44) and OR myeloperoxidase + = 4.23; 95%CI = 1.84-

9.73 235. A previous history of hay fever was found to be protective with an OR of 0.32; 95%CI = 0.21-0.50. Alavanja et al. (1992) examined preexisting lung disease in nonsmoking women and the risk of lung cancer. The OR = 1.7 for the risk of adenocarcinoma and previous lung disease; the overall OR for all lung cancer types was 1.8. The OR’s for nonsmokers were significant for lung cancer risk; OR asthma = 2.7and OR pneumonia = 1.5. The OR for emphysema was 2.6 and tuberculosis, OR = 2.0, for former smokers. The authors found

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an attributable risk percent (16%) among women that were nonsmokers and had a previous history of emphysema, asthma, pneumonia, and tuberculosis 235. Further investigation is warranted as the biologic role of respiratory diseases in lung cancer etiology is unclear as the evidence is not consistent.

Treatments for Lung Cancer How lung cancer spreads throughout the body can be classified into three categories: intrathoracic (local), lymphatic (regional), and hematogenous (distant). The sequence of the cancer growth is sporadic and does not necessarily follow any particular order 72. Small cell carcinoma (oat cell) has the greatest probability of distant spread as compared to non small cell lung cancer; adenocarcinoma of the three NSCLC types has the highest incidence of distant spread or metastasis 2, 4, 40. Depending upon the diagnosis of the lung cancer histologic type, stage, grade and the health of the patient, a clinical decision is made by the physician how the treatment will proceed. These treatment decisions are based on years of empirical data, research, and clinical trials; the standards of care established by the medical community are overseen by several organizations such as the American Medical Association, the American College of Surgeons, the National Cancer Institute, and the American College of Radiology 33.

Confined to the Lungs NSCLC confined to the lung is considered early stage disease and is treated with a

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surgical resection 72. Postoperative radiation therapy with or without chemotherapy is recommended for the treatment of microscopic disease 135. The patient’s health and comorbidities are other considerations if the patient is a surgical candidate. Radiation Therapy is then the treatment of choice if the patient cannot tolerate surgery. Early stage small cell lung cancer is considered limited disease spread and the primary treatment is chemotherapy plus concurrent radiation therapy. There has been current interest in surgical procedures for limited stage SCLC; according to Anraku and Waddell (2006) when the disease is confined, surgery improves the local control and increases survival 54. The authors also note that continued research via clinical trials is warranted to confirm long term results. Local Spread Once the tumor has spread beyond the hemithorax and there is mediastinal lymph node metastasis, the treatment options include chemotherapy and radiation therapy; at this stage surgery is contraindicated. Surgery could be an option in cases of limited mediastinal lymph node involvement in combination with chemotherapy or radiation therapy 72. Prior to the advent of CT scans and the ability to detect mediastinal lymph node metastasis, it has been estimated in the literature that 30% of all NSCLC patients would have received a surgical resection unnecessarily by current medical standards 72.

Distant Spread Extrathoracic, distant, or hematogenous spread involves the growth of lung cancer into multiple organs. Treatment of extensive disease for non small cell lung cancer and

72

small cell lung cancer is chemotherapy alone 135. The anticancer agents treat the disease systemically or throughout the body. The future direction for treatments includes targeting therapies, i.e. the treatment of cancer cells treated with bio-agents that attack the cells at the molecular level 135.

Lung Cancer Relapse In the case a lung cancer relapse, the stage, grade, histologic type, and patient condition once again determines next steps in treatment options. Angeletti, et. al. (1995), noted that surgery is warranted in the case of an early stage lung cancer relapse or a second locally confined primary in the lung 236. Although long term survival and local control has not been validated, the authors suggest this as a viable option to increase patient survival. Another approach suggested by Johnson, et. al. (1990) suggested a combination of cyclophosphamide, doxorubicin, and vincristine or etoposide or both vincristine and etoposide for SCLC.

Complications of Lung Cancer The lungs are highly vascular and are supplied by a system of lymphatic glands so the greatest complication of lung cancer is the spread of cancer through different tissue and organs of the body (metastasis). Another major complication of lung cancer is the reappearance of the disease in the form of another primary neoplasm or the development of a secondary tumor 72. According to the American Cancer Society, lung cancer relapse

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commonly occurs within two years even in the event of a positive treatment course 2. Complications are associated with each treatment regimen; in particular chemotherapy agents have been shown to decrease survival resulting in the early termination of clinical trials 237. Venuta, et al., 2006, found in a retrospective study based on one hundred and thirty-nine patients (100 males and 39 females), preoperative functional parameters, type of operation, associated disorders, staging, induction regimen (chemotherapy alone or combined with radiation therapy), all added to the complication rate for surgical procedures 238. After multivariate analysis for morbidity and mortality and controlling for age and lung functional volume; the results were not statistically significant. The complications for radiation therapy lung cancer treatments include decreased ventilation function, hemoptysis, and local relapse. Historically, the major disadvantage to the use of radiation therapy was the amount normal tissue that had to be irradiated thereby reducing lung function in a lung already compromised by cancer. Advances in radiation therapy treatment modalities, e.g. Intensity Modulated Ration Therapy (IMRT), Image Guided Radiation Therapy (IGRT), Respiratory Gated Radiation Therapy (RGRT), all allow for techniques to minimize motion and to increase the precision of the target or tumor coverage 239-241. Underberg, et. al., 2005 found a 50% reduction in the primary tumor volume irradiated with the new treatment techniques. This means that the newer radiation therapy techniques can offer a consistently smaller irradiation volume so decreased toxicity can be expected; any longer term effect on survival would have to be investigated 242.

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Lung Cancer Treatment Modalities The type of lung cancer treatment modality is dependent upon several factors that include the histological type of lung cancer, the size of the tumor, the location, extent or degree of regional spread of the disease, and the general condition of the patient. Many of the treatment modalities, e.g. radiation therapy, surgery, chemotherapy, photodynamic therapy, can be used separately or combined to treat the cancer239-241. As there are several treatment options, there are at the very least two main goals that are anticipated upon completion of the treatments. First, there is an expectation that if a complete cure is not achieved, the progression of the tumor has been confined. This should have an overall effect of increased survival. Secondly, there is an expectation that the symptoms of the disease will be diminished for the patient, thereby improving quality of life issues. In the next several sections in this chapter, current and emergent technologies will be expanded upon.

Radiation Therapy Radiation therapy or radiotherapy utilizes high energy rays to kill cancer cells 4, 47

. The radiation is delivered to a very specific region of the diseased lung, with the goal

of a minimal radiation dose given to normal tissue. Prior to a surgical procedure, radiation can be given to decrease the tumor size and destroy peripheral, microscopic disease surrounding the tumor 4, 239-241. Radiation therapy can also be used for the relief of lung cancer symptoms, such as shortness of breath. There are several radiation therapy

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treatment options for lung cancer; external beam therapy and radioisotope therapy (brachytherapy) 6. External beam is the application of a radiation beam from an external source, e.g. linear accelerator, cyclotron, to the affect site of cancer. Recent advancements as discussed in ―Complication of Lung Cancer‖ section of this chapter reviewed the newer technologies to reduce radiation damage of the normal tissue, thereby preserving lung function 72. One of the newest and most costly (1.2 billion US dollars) radiation therapy treatment involves treating with a proton beam to reduce radiation induced morbidities. Protons deposit their energy maximally at a specific distance from the patient surface 72. This maximum deposition occurs at the Bragg Peak of the proton; the amount of radiation deposited is dependent upon the energy of the proton (a charged ionizing particle) and the medium or material of interaction. There is minimal interaction with normal tissue and decreased lung function toxicity. Endobronchial brachytherapy involves the application of a radioactive material to the affect site in the lung; a commonly used radioisotope is Iridium-192 72. Normally the treatment is done for a more circumscribed area of the lung (less than 10 centimeters in length) as compared to external beam radiation. The application of the radioactive source can be done by two methods; either a high dose rate application (taking several minutes) or by the low dose method (can take several hours) 72. For the high dose rate (HDR) method, the radioactive source is introduced into the lung tumor via a flexible catheter. This catheter is place prior to the HDR during a bronchoscopic procedure. The treatment goal is to minimize any normal tissue damage and deliver a therapeutic dose (3 to 10

76

Gray per fraction) 72. The advantage of this technique is an increased local control of the tumor progression and it is less invasive than a surgical procedure with the associated postoperative morbidities. The main disadvantages to this technique is fistula formation and fatal hemoptysis 243.

Chemotherapy Chemotherapy is another lung cancer treatment option, as shown in Table 5, which uses drugs to obliterate cancer cells. The purpose of chemotherapy is to kill the cancerous cell or interrupt the cancer cell cycle, thereby preventing the growth of the tumor. One of the harmful effects of chemotherapy agents is the drug(s) destroy normal cells in the process of destroying the cancer cells. The destruction of normal cells/tissue can cause various side effects that include nausea, vomiting, diarrhea, increased susceptibility to infections, and in some instances, death 9. The various chemotherapy drugs are administered either by infusion, orally, or as a combination of both during a treatment. NSCLC and SCLC can both be treated with chemotherapy agents; the optimum or most effective drugs for the treatment of lung cancer are platinum-based drugs, cisplatin and carboplatin 135. Other non-platinum based chemotherapy agents include docetaxel, paclitaxel, gemcitabine, and irinotecan; these can be used in conjunction with cisplatin and carboplatin during the treatment regimen. For further information, Appendix III Table 75 contains a listing with chemotherapy drugs typically used in the treatment of lung cancer, the type of agent, and major side or adverse effects.

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Typically, chemotherapy is used as an adjunct or secondary treatment to surgery or radiation therapy, dependent upon the histological type and stage of the disease. In the case of small cell lung cancer, chemotherapy is the treatment of choice because the procession of the disease is typically widespread throughout the body when the diagnosis of lung cancer is made 4, 135. Small cell lung cancer accounts for approximately 20 to 25% of incident lung cancer cases diagnosed 13. Of all SCLC diagnosed, only 40% of the cases have the disease limited to the chest cavity (thorax). The regimen of choice is chemotherapy plus radiation therapy. In some instances, prophylactic irradiation of the brain is given to treat micrometastasis; which is the early spread of the cancer to the brain. The five year survival rate for limited stage SCLC is 15 – 25%. The median survival for patients that do not receive chemotherapy in combination with radiation therapy is 6 to 12 weeks 237. If the SCLC has developed into an extensive stage, the recommendation is to use chemotherapy alone; these cases account for 60% of all the newly diagnosed SCLC 237. When a case of SCLC is diagnosed at this stage, the disease has normally progressed or metastized to the brain, liver, bone, and/or bone marrow. According to Carney in his New England Journal of Medicine article,‖ Lung Cancer Time to Move on from Chemotherapy‖, he states that over the past twenty years, all the chemotherapy drugs combinations and varying treatment regimens, the most significant improvement in survival was on average two months 244. At advanced stages of lung cancer, clinical trials are still being evaluated for efficacy 54, 59, 245-249. Gerold Bepler of the H. Lee Moffitt Cancer Center and Research Institute, noted

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that there is a renewed interest in the development of new strategies for chemotherapy to increase survival for NSCLC patients 250, 251. This has come about due to advances in genetic research and the identification of genes on the chromosomes that have been identified as prognostic factors in the treatment of lung cancer. The level of the genetic material present in a lung cancer case’s biological makeup can determine which specific chemotherapy drug or combination of chemotherapy drugs will be most effective to treat the cancer 250. An increased level of two genes, RRM1 and ERCC1, has been shown to decrease the effectiveness of chemotherapeutic drugs 251. In the clinical trial, the International Adjuvant Lung Cancer Trial, Olaussen et al. (2006), demonstrated that patients with ERCC1- negative tumors had increased survival (adjusted hazard ratio = 0.65, 95% CI 0.50 – 0.86; p-value = 0.002); whereas patients with ERCC1 – positive tumors (adjusted hazard ratio = 1.14; 95% CI 0.84 – 1.55; p-value = 0.40) 252. Table 5 gives a summary of the recommendations found in Collins, et al 20074 and describes the primary or first choice of treatment for a particular lung cancer type, i.e. Non Small Cell Lung Cancer and Small Cell Lung Cancer, and stage of the disease. The secondary treatment modality recommendations and the associated five year survival rates are also listed in Table 5.

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Table 5: Lung Cancer Treatment Recommendations Stage

Primary Treatment Modality

Secondary Treatment Modality

Survival Rate

Non Small Cell Lung Cancer (NSCLC) I II

III A (resectable)

III A (unresectable)

III B (pleural effusion) or IV

Surgery (Resection) Surgery (Resection)

Preoperative chemotherapy followed by surgical resection (preferable) or surgical resection Chemotherapy plus concurrent radiotherapy (preferable) or chemotherapy followed by radiotherapy Chemotherapy with 2 agents for 3 or 4 cycles (preferable)

Chemotherapy† Chemotherapy with or without Radiation Therapy† Chemotherapy with or without Radiation Therapy

None

Surgical resection of solitary brain metastasis and surgical resection of primary (T1) lesion

5-Yr survival rate, > 60 – 70% 5-Yr survival rate, > 40 – 50%

5-Yr survival rate, 15 -30%

5-Yr survival rate, 10 – 20%

None

Median survival, 8–10 mo 1-Yr survival rate, 30–35% 2-Yr survival rate, 10–15%

None

5-Yr survival rate , 10–15%

None

5-Yr survival rate, 15 – 25%

Small Cell Lung Cancer (SCLC) Limited Disease‡

Extensive Disease‡

Chemotherapy and concurrent Radiation Therapy Chemotherapy

None

5-Yr survival rate, < 5%

* All chemotherapy regimens include either cisplatin or carboplatin. A complete list of clinical trials is available at http://www.cancer.gov. and up-to-date approaches to the treatment of non–small-cell and small-cell lung cancer are available from the National Comprehensive Cancer Network at http://www.nccn.org. † This regimen is based on data from the International Adjuvant Lung Cancer Trial, which demonstrated a small but significant survival advantage with cisplatin-based adjuvant therapy. Physicians should strongly consider such therapy for appropriate patients. ‡ Prophylactic cranial irradiation is recommended for all patients with a complete response to initial therapy. Source: Adapted from Collins, et. al., 2007

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Surgery Surgery removes lung cancer or the tumor during an operation. The operation or surgical procedure is based on the histology or type of lung cancer and where the tumor is located. A surgical procedure is not recommended for small cell lung cancer, unless the disease is very circumscribed and is not too advanced at the time of diagnosis 4. Surgery is recommended by the American College of Chest Physicians and the American Joint Commission on Cancer 4, 9, 253 for NSCLC. According to Rivera, et. al., (2003), Stage I and Stage II have the optimum prognosis, with a average five year survival rate of 60% to 70% for Stage I and a greater than 40 – 50% for Stage II 9. Other considerations for a surgical procedure for the excision of a lung tumor include the location of the tumor, i.e. the tumor may be too close to the heart or the trachea, if the patient is a surgical candidate and can withstand the surgical intervention, and the stage or extent of the disease 135. If the disease is too extensive throughout the body, a surgical procedure to remove the lung tumor is not recommended. Radiation Therapy and/or chemotherapy may be other options if surgery is not indicated 4, 9, 135. Different types of surgical procedures include 1) wedge resection, 2) segmentectomy, 3) lobectomy, 4) lymph node removal, and 5) pnumonectomy. A wedge resection consists of the removal of part of the lung; it is used when the tumor is confined to a particular location in the lung. When the tumor is removed, a portion of the healthy or non diseased lung is also removed. This is done in an attempt to eliminate any microscopic disease around the periphery of the tumor bed. A segmentectomy is the

81

procedure where a greater margin is taken around the tumor bed. A lobectomy entails the removal of one of the five lobes of the lung; the right lung has three lobes and the left lung has two lobes. This type of surgical resection involves the surgeon making an incision on the patient’s side between the ribs. Once the incision has been made the surgeon then spreads the ribs apart; this makes the lung tumor accessible for removal. A video-assisted lobectomy (VATS) employs a small video camera (a thorascope) inserted into the chest cavity; the images received form the thorascope guides the surgeon to the operative area in the chest. This particular procedure minimizes the bleeding and complications of the surgery; the patient stay is reduced from five to seven days to two to three days. A lymphectomy or lymph node removal can be accomplished during the surgical procedure. The lymph nodes are examined by the pathologist for signs of disease; if the nodes were positive, this would be an indication of disease spread. In the case of positive nodes, the surgeon may opt not to remove any of the tissue as the stage of the disease has progressed and surgery is not an indication 4, 54, 59, 253. A pnumonectomy is the complete removal of the entire lung. This procedure can only be performed if the patient’s physical condition and breathing capacity can tolerate this extensive surgical procedure and would not compromise the patient’s quality of life. A pnumonectomy is recommended for centrally located tumors and tumors that involve more then one lobe. The removal of the lung is warranted when the tumor has spread throughout the lung but has not metastized to other parts of the body. The complications from surgery may include internal bleeding, infection, lymohocytopenia (low white

82

count), and possible reoccurrence of the disease.

Combination Therapy Treatment modalities are combined in an effort to increase the length of survival for lung cancer patients. The standard of practice for small cell lung carcinoma with limited disease (no evidence of spread) is chemotherapy drugs concurrent with radiation therapy 4, 135. According to Anraku and Waddell (2006), surgery can be warranted under certain conditions for early stage SCLC. Chemotherapy with a combination of surgery for patients with T1-2 N0 SCLC may enhance local control but the authors also note that clinical trials are needed to validate these results 54. Surgery and chemotherapy can be offered to patients that have a mixed tumor type (SCLC and NSCLC components) as the anticancer agents are less effective against NSCLC in the limited or early stages of the disease 54. Stage I and Stage II NSCLC use the combination of surgical resection, radiation therapy, and/or chemotherapy. Surgery is the primary treatment with radiation and/or chemotherapy as the adjuvant therapy. Other treatment regimens include surgery with or without preoperative chemotherapy for resectable Stage IIIA with the adjuvant therapy of chemotherapy with or without radiation therapy. The five year survival rate is 15 to 30% for this course of therapy4, 9. The recommendation for Stage IIIA unresectable NSCLC is chemotherapy with concurrent radiation therapy or radiation therapy treatments after the course of chemotherapy treatments. The five year survival rate declines for this combination of modalities to 10 to 20% 4, 9, 135. Patients with Stage IIIB

83

(pleural effusion) or IV are given chemotherapy, resection of a primary T1 tumor and primary brain metastasis.

Emergent Modalities The optimum treatment for lung cancer is based on stage and grade of the disease4, 144, 243, 249, 253, 254. There are several recently developed endoscopic modalities for the treatment of early stage lung cancer. 243. Although the newer treatment techniques offer a less invasive technique, decreased perioperative morbidity and reduced cost as compared to conventional modalities, the techniques and methods require validation 4, 243. Endoscopic therapies such as photodynamic therapy, brachytherapy, neodymium yttrium aluminum garnet laser, electrocautery, and cryotherapy; offer an alternative in the treatment of early stage lung cancer. These modalities can be applied during a procedure known as a fluorescence bronchoscopy. Pathological changes in the appearance of normal lung tissue can be detected during bronchoscopy utilizing fluorescence. Normal tissue fluoresces (emission of light) at varying energy levels when compared to cancerous tissue; this difference in energy levels is seen by the human eye as differences in color 243

.

The main disadvantage to this detection method is the high false-positive rate;

inflammatory processes or trauma can cause changes in the light patterns that could be perceived as cancerous 243, 255. Lam, et al., 1993, reported a sensitivity of 72.5% and a specificity of 94% in the detection of advanced dysplasia 243, 255. When comparing fluorescence bronchoscopy to conventional bronchoscopy, the conventional technique

84

had a sensitivity of 48.4% 255. A multicenter trial comparing the two techniques found fluorescence bronchoscopy detection rate of invasive lung carcinoma of 95% as compared to conventional bronchoscopy with a 65% detection rate 256. Photodynamic therapy (PDT), as a treatment for lung cancer, has been used in conjunction with fluorescence bronchoscopy 243. PDT involves targeting the lung tumor cells with a photo-sensitizing agent and an application laser light to the affected area during a bronchoscopy. The laser light, typically 630 nanometers, activates the chemical sensitizer producing a photochemical reaction at the cellular level. This results in the destruction of the tumor cells by an oxidative process 243. Lung cancer treatments can be done by a neodymium yttrium aluminum garnet (Nd: YAG) laser. This treatment has been done for palliation purposes but the literature does not support a significant contribution in the treatment of early stage lung cancer 243. Gerasin et al. (1990), did report success with this technique for early stage lung cancer in the contralateral lung when a lobectomy is contraindicated 257. Scientific advancements in the fields of chemotherapy, surgery, and radiation therapy involving tumor development (carcinogenesis) and lung cancer progression have been possible with the discovery of genetic materials that are involved in the disease process 4, 9. There has been recent evidence that a drug, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, has been effective in the treatment of bronchialveolar cell carcinoma (a NSCLC type). This particular cancer type is common in women and in non smokers. The drug causes the shutdown of the epidermal growth factor receptor

85

protein, thereby preventing the development of the cancer.

Conclusions and Assessment of the Literature The purpose of this research is to test the hypothesis of a survival difference in women with lung cancer as compared to men, dependent upon treatment modality (surgery, chemotherapy, radiation, or combination), histological type, and stage of disease. An extensive literature search and review was preformed to investigate the variables that could be involved in the associated lung cancer treatment received and how that treatment decision influences women’s survival as compared to men. There is limited research regarding the risk of being a woman with lung cancer and the treatment received as compared to men. As described in the literature review, there are no studies combining the specific treatment modality received or combinations of treatment received, gender, stage, grade, morphology and demographic factors with respect to survivorship. Visbal et.al. (2004) noted the Relative Risks between males and females and survival adjusting for stage, histological, and treatment type. Although there were significant results for stage (Stage IIIB RR = 1.22, 95% CI = 1.02, 1.46 and Stage IV RR = 1.29, 95% CI = 1.15, 1.44), there was no significant results for treatment type received. Also the authors did not mention if there were any combined treatments of chemotherapy, radiation, or surgery 17. In the article by Radzikowska et. al. (2002), (n = 11,479) a multivariate survival analysis was performed based on age (categorized into two groups; group I < 50 years old, group II > 50 years old), gender, performance status, clinical

86

stage of lung cancer (four stages), histology (adenocarcinoma, squamous, SCLC, and other) and two treatment types (surgery and non-surgical) 15. In the Cox-Proportional Hazard’s model, the relative risk of death was adjusted to age, gender, histological type of lung cancer, performance status, and stage of the disease. The RR’s were given with no confidence intervals; any truly significant results could not be evaluated. The reported RR’s were all greater than 1.0 (squamous cell RR = 1.09, SCLC RR = 1.42, Other Histological Type RR = 1.46) with the exception of the reference group (RR = 1.0). There were significant p-values, all values < 0.004 with the exception of the histological type for squamous cell (p-value = 0.29). Lung cancer was six times more frequent in males versus females and women with lung cancer were younger and smoked less than males with lung cancer. There are many histological types of lung cancer, finding an optimum treatment regimen that will increase survival for a particular lung cancer type is challenging. Each histological type has its own medical intervention that can include any combination of surgery, radiation therapy, and/or chemotherapy. Gender-specific incidence and survival rates were shown to be different for the lung cancer types. In the article by Thomas et. al. (2005), ―Lung Cancer in Women: emerging differences in epidemiology, biology, and therapy‖ 48, as the authors noted ―women are at an increased risk for lung cancer than men‖. The gender differences placing women at a greater risk were reported to include molecular variables such as different metabolism of tobacco-related carcinogens, possible association with human papilloma virus (HPV) infection, and that women have less DNA

87

repair capacity (DRC); the authors also noted that women had better survival outcomes stage for stage than men 48. Presently, there are no published quantitative results that show whether there is a statistically significant difference regarding survival due to a particular treatment for women as compared to men having the same histological type, grade and stage of lung cancer. The statistical methods performed in the articles cited in the literature review did not evaluate interaction effects of the independent variables; this is a major limitation as any gender specific differences based on any moderating variables could not be evaluated. Performing this research provides the scientific evidence to answer the question concerning gender, stage, grade, morphology, age, martial status and race and their impact on survival. Findings of treatment differences by major histological types are presented in this dissertation. In conclusion, the goal of this research was to provide a statistical and biologically plausible model demonstrating gender differences in lung cancer survival exist based on the treatment received.

88

CHAPTER III: PROCEDURES AND METHODS Introduction The purpose of the research presented in this dissertation was to determine if gender differences exist for a treatment(s) utilized for lung cancer and if that treatment(s) received impacts gender specific survival. The epidemiologic study design was based on a historical cohort of lung cancer cases drawn from population based state-wide cancer registries. Each cancer registry were members of the North American Association of Central Cancer Registries, NAACCR 258. The participants were men and women with newly diagnosed/incident primary lung cancer diagnosed between January 1, 2000 and December 31, 2004. All lung cancer cases selected were pathologically confirmed and classified on the four major histological types of the disease, i.e. adenocarcinoma, squamous cell carcinoma, large cell carcinoma and small cell carcinoma.

Aims/Hypothesis Aim 1: The first aim was to determine if men and women with the same histologic type, stage, and grade of lung cancer received the same treatment type. Any effect, such as any interaction that the covariates (histologic type, stage, and grade) may exert on the relationship between gender and treatment received must be evaluated. It was important to establish if there were treatment differences’ dependent upon gender. If the lung cancer treatment is gender dependent, this may impact gender specific survivorship.

89

Hypothesis 1: Women with the same histological type, stage and grade of lung cancer will receive the same treatment modality as compared to men with the same histological type, stage and grade of lung cancer. Aim 2: The second aim is to evaluate the overall relationship between survival and gender for the lung cancer cases. The goal is to obtain an assessment of the overall survivorship by gender. Hypothesis 2: There is a statistically significant difference in survival in women with lung cancer as compared to men with lung cancer. Aim 3: The third aim of this study is to expand the investigation of treatment modality differences and gender-specific survival. The goal is to determine if men and women with lung cancer grouped or stratified by treatment modality, histologic type, stage, and grade exhibit or demonstrate gender-specific survivorship. Hypothesis 3: Women with the same histological type, stage, grade of lung cancer, and the same treatment modality differ significantly in survival as compared to men with the same histological type, stage, and grade of lung cancer, and the same treatment modality.

Participant Description and Case Identification The study participants in this research are primary lung cancer cases drawn from population based state-wide cancer registries. Data on cancer cases and cancer deaths are collected, managed, and analyzed by a system of state-based cancer registries 1. The

90

majority of state cancer registries are members of the National Program of Cancer Registries 259; the NPCR was established in 1992 by the Cancer Registries Amendment Act 259. The NPCR is administered by the Centers for Disease Control and Prevention (CDC) and is under the Division of Cancer Prevention and Control 259. Prior to the establishment of the National Program of Cancer Registries, there were ten states with no cancer registry; today there are forty-five states with cancer registries to include the Virgin Islands, the Republic of Palau, Puerto Rico, and the District of Columbia 258. Another international cancer registry organization that certifies NPCR is the North American Association of Central Cancer Registries 258. NAACCR was established in 1987; it represents state cancer registries and professional organizations such as the American College of Surgeons, the American Cancer Society, and the Public Health Agency of Canada 258. Other responsibilities of the NAACCR or known as the ―central cancer registry‖ are to monitor and certify state cancer registries; this ensures the data collection methods used by each state registry are complete, accurate, and done on a timely basis. The each state registry that is a member of NAACCR submits cancer case data obtained from medical facilities, e.g. hospitals, surgical centers, laboratories, outpatient facilities, physician offices, and radiation therapy centers, to the central cancer registry. Cancer information is collected or abstracted in a standardized format into highly specified field arrangements 258. The standardization of field information allows for the intercomparison of data within the state, with other states, and on a national level 258, 259

.

91

One of the first steps in the selection of the primary lung cancer cases for this research study was to identify the eligible cancer registries. The criteria for the state/state cancer registries selected had to be established. Each selection parameter or criterion was critical; the ultimate goal being selection criteria that could be utilized to generate a dataset free from bias. The criteria for selection of a state/state cancer registry (Table 6) for this research were as follows: Table 6: Criteria for State/State Cancer Registry Selection 1. The registry must exist in a state in the United States of America, 2. The registry must be population based, 3. Each registry must be selected from the four US regions as defined by the US Census Bureau, 4. The individual lung cancer cases must be randomly selected, as each lung cancer case in the state cancer registry must have an equal chance of being included or excluded in the registry, 5. The cancer registry must be defined as a ―passive‖ registry, 6. The data must include primary lung cancer cases diagnosed between 1-1-2000 and 12-31-2004, a five year time frame, 7. The state registry must be a member of NAACCR, 8. Each state registry must meet the criteria of the ―Standards for Cancer Registries: Standards for Completeness, Quality, Analysis, and Management of Data‖ 258 , and 9. The state cancer registry must have achieved NAACCR certification (a minimum of 3 years gold certification and maximum of 2 years silver certification – see Table 6. 10. Each state must be randomly selected as not to introduce selection bias, 11. The data must be accessible and retrievable to the researcher conducting the study.

92

The NAACCR guidelines for certification are measurable standards and each year state registries obtaining the goals for ―Completeness of Data‖ are awarded a gold or silver designation (if the goals were achieved) by NAACCR. The selection of states for the research study began with a process as outlined in Figure 10.

50 USA States

37 USA States (50 – 13 SEER States)

29 USA NAACCR States (Reporting – Non-Reporting States)

14 USA States (Meet Certification Guidelines)

8 USA States (Randomly Selected) Figure 10: State Selection Process

93

This goal of this research ―selection criteria‖ protocol was designed to minimize bias. If one state is selected over another state it must be purely by chance thereby eliminating any possible influences or bias. In other words, each state cancer registry must have an equally likelihood of being selected; one state may be inherently different and this difference would tend to diminish with the having all the states randomly selected. Another source of bias may be introduced by the investigator if that investigator selected a particular state over another state due to personal or unscientific reasons – the random process would be invalid. . The first text box of Figure 10 represents individual lung cancer cases from the fifty US states which serve as the population from which the final research data set (Criterion 1) was drawn. The research protocol was developed as to include lung cancer cases selected from population based cancer registries (Criterion 2). The fifty states are sub-divided into geographic regions by the US Census Bureau (Criterion 3). The Bureau identifies four major US regions (South, Midwest, West, and the Northeast) which correspond to regional populations from which the lung cancer cases will be selected. As discussed in the literature, population characteristics can differ with geographic location 155, 260-262

and it is critical that the individuals selected are randomly selected lung cancer

cases as not to introduce bias (Criterion 4). Of the 50 states, 13 states were excluded (text box 2 of Figure 6), leaving 37 NAACCR states. The states excluded were members of SEER and those SEER states were: 1 - Connecticut, 2 - Hawaii, 3 - Iowa, 4 - Louisiana, 5 - New Jersey, 6 - New

94

Mexico, 7 - Utah, 8 - Georgia (multi-country areas of Atlanta & rural Georgia), 9 Michigan (Detroit), 10 - California (San Francisco-Oakland, San Jose-Monterey, Los Angeles county, remaining counties of California), 11 - Washington (Seattle-Puget Sound), 12 - Arizona (American Indians), and 13 - Alaska (Alaskan Natives). SEER is an active registry system and those states under SEER do not meet selection criterion 5. This is important as there are two main types of cancer registries, passive and active, that differ in the method data are collected. An active cancer registry collects the data from the medical facilities whereas a passive registry has the data sent to the registry from the medical facilities (reporting facilities) that are part of the state wide system. An example of an active state cancer registry would be a member of SEER such as the Kansas Cancer Registry. NAACCR cancer registries are passive; some of the state cancer registries are listed in. In this research, passive registries were only selected (Criterion 5). Having only passive registries served the following purpose: the ―passive‖ classification aided in the standardization of the states selected; this helped to minimize selection bias by only selecting states that have a similar reporting mechanism and reporting criteria. The thirty-seven states were evaluated (text box 3 Figure 10.) for their NAACCR status. Eight of the thirty-seven states were not members of NAACCR for a portion of the years under study (2000-2004), thereby excluding them from participation in this research; 29 US NAACCR states remained (Criteria 6 and 7). Selection Criterion 6, restricting the time period under study (a 5 year range), will help to reduce any temporal differences associated with lung cancer treatments. A temporal bias may be introduced

95

when studying extended time periods or ranges. Treatment modalities have drastically changed over the past 20 years, and trying to determine treatment effects over that 20 year time period would be more difficult to ascertain. Survival differences during this 5 year range are of particular concern; the treatment modality utilized to treat the lung cancer case should be more consistent and this could impact survivorship. Therefore, the date/time range established for lung cancer case data inclusion starts January 1, 2000 and ends December 31, 2004. The reporting system used for the cancer registries of interest for this research is based on the ―Criteria and Standards for Eligibility of NAACCR Registry Certification and CINA Combined Rates‖ (Criterion 8). The criteria and the standards are displayed below in Table 7. Using this criterion, fifteen (15) states, as shown in Table 8., were excluded and fourteen (14) NAACCR states remained from the 29 states (text box 4 of Figure 10.). The selection criteria for state inclusion were based on the grading scales established by the central cancer registry as outlined in Table 7 below (NAACCR Criteria and Standards for Gold/Silver Certification). NAACCR certifies for ―High Quality Incidence Data‖ and the exact protocol followed by the central cancer registry is contained in the North American Association of Central Cancer Registries, Inc. Standards for Cancer Registries Volume III, ―Standards for Completeness, Quality, Analysis, and Management of Data‖. For example, the criteria and standards are used to evaluate characteristic variables of the tumor such as tumor morphology (histology and behavior), stage, grade, and the method of diagnostic confirmation. The standards are used to assess

96

the quality of the information in the individual state cancer registry, completeness of the data reported, and timeliness of reporting 258. In summary, a total of fifteen (15) of the twenty-nine (29) states did not meet the selection criteria as outlined in Criterion 9; thereby excluding those states from participation (see Table 7).

Table 7: NAACCR Criteria and Standards for Gold/Silver Certification

Source: Standards for NAACCR Cancer Registries: Standards for Completeness, Quality, Analysis, and Management of Data Note: DCO = Death Certificate Only

Table 8 lists the twenty nine NAACCR states from the four US Census Bureau defined regions and their certification status over the study 5 year time period (2000 – 2004).

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Table 8: Annual NAACCR Certification Designation Five Years of Certification - States (29) by Region (4) REGION

WEST

STATE

2000

2001

2002

2003

2004

Annual Incidence

Average Annual Cases

COLORADO

gold

silver

gold

gold

gold

54.9

2011

IDAHO

gold

gold

gold

gold

gold

55.2

683

MONTANA

silver

silver

silver

gold

gold

66.5

663

NEVADA

gold

gold

gold

gold

gold

76.9

1622

OREGON

gold

gold

gold

gold

gold

69

2489

ALABAMA

silver

silver

gold

silver

gold

75.2

3569

silver

silver

81.1

2404

silver

gold

gold

76.6

649

ARKANSAS

silver

DELAWARE

SOUTH

FLORIDA NORTH CAROLINA OKLAHOMA SOUTH CAROLINA

gold

gold

gold

gold

gold

74.6

15838

gold

silver

silver

silver

gold

68.5

5611

gold

gold

gold

83.9

3068

gold

gold

gold

74.7

3111

gold

gold

68

12162

silver

silver

TEXAS

MIDWEST

WEST VIRGINIA

gold

gold

gold

gold

gold

87.7

1915

ILLINOIS

gold

gold

gold

gold

gold

72.8

8836

INDIANA

silver

gold

gold

silver

gold

80.6

4931

KANSAS

gold

gold

gold

gold

66.5

1841

MINNESOTA

gold

gold

gold

gold

58.8

2843

MISSOURI

gold

silver

gold

gold

gold

78.6

4731

NEBRASKA

gold

gold

gold

gold

gold

62.4

1118

OHIO

silver

silver

silver

silver

74.6

8993

silver

58.7

486

65.9

3700

SOUTH DAKOTA WISCONSIN

gold gold

MAINE

NORTHEAST

gold

gold

gold

gold

gold

gold

gold

79.1

1183

MASSACHUSETTS

gold

gold

gold

gold

gold

70.5

4764

NEW HAMPSHIRE

gold

gold

gold

silver

gold

67.7

860

PENNSYLVANIA

gold

gold

gold

gold

70.4

10292

RHODE ISLAND

gold

gold

gold

gold

73.8

860

silver

gold

63.9

418

gold

VERMONT

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Of the 29 states, 15 states were excluded as the state had to meet at a minimum three years of gold certification and a maximum of 2 years of the silver certification as defined the NAACCR criteria (see Table 7.) for the five year time frame under study (Criterion 9). Table 9 lists the final 14 states; the minimum number of states in a region (the South and the Northeast) was three with a maximum number of states of four in the West and Midwest. Table 9: Annual NAACCR Certification Designation by Region and State (14) REGION

WEST

SOUTH

MIDWEST

NORTHEAST

STATE

2000

2001

2002

2003

2004

Annual Incidence

Average Annual Cases

COLORADO

gold

silver

gold

gold

gold

54.9

2011

IDAHO

gold

gold

gold

gold

gold

55.2

683

NEVADA

gold

gold

gold

gold

gold

76.9

1622

OREGON

gold

gold

gold

gold

gold

69

2489

FLORIDA

gold

gold

gold

gold

gold

74.6

15838

SOUTH CAROLINA

silver

silver

gold

gold

gold

74.7

3111

WEST VIRGINIA

gold

gold

gold

gold

gold

87.7

1915

ILLINOIS

gold

gold

gold

gold

gold

72.8

8836

INDIANA

silver

gold

gold

silver

gold

80.6

4931

MISSOURI

gold

silver

gold

gold

gold

78.6

4731

NEBRASKA

gold

gold

gold

gold

gold

62.4

1118

MASSACHUSETTS

gold

gold

gold

gold

gold

70.5

4764

NEW HAMPSHIRE

gold

gold

gold

silver

gold

67.7

860

RHODE ISLAND

gold

gold

gold

gold

gold

73.8

860

The fourteen states were distributed from the four US geographic regions – from each region the intent was to randomly select two states (Criterion 10). As stated previously, the states were selected at random as not to introduce bias. The reasons for

99

selecting two states from each region were: 1. At least two states were needed from each region to measure or account for variability within the regions. 2. Including all fourteen states was not feasible due to limited resources, e.g. cost considerations, manpower, and time constraints. In the selection of the states from the four US geographic regions, a random sample selection was made utilizing a SAS program; these samples represent the population from which they were drawn (all US primary lung cancer cases). To account for any variability of the population within each region, more than one state for the region had to be selected. At a minimum, at least two states from each region must be selected in order to account for any variability within the region. As part of the selection criteria the data must be accessible (Criterion 11); not all states consented to having the data distributed to an outside individual. Logistical issues, such as data unavailability would prevent the selection of a state registry. An additional logistical issue that could be encountered could be - although a particular state registry may meet NAACCR requirement for certification, the state reporting system may not report a variable needed for the research under study. The final step was to call each of the state registries and request the procedure the particular state registry utilized for a data request (Criterion 11). West Virginia was hesitant to participate due to concerns for the protection of patient privacy and was requiring the author to send the author’s Curriculum Vitae (CV), the committee members CV’s, and the complete IRB application

100

that was submitted to the University of South Florida (the application contained confidential information about the author and committee members); the registry would not accept the official IRB approval letter. Also, West Virginia does not/did not report the complete date listed for ―date of last contact‖ a variable of interest in this research; the date is reported by year and does not include the day and month. As West Virginia did not meet selection Criterion 11, South Carolina was selected to participate; the resultant 8 states selected are given in Table 10. Additionally, after fourteen months of requesting data from the Missouri Cancer Registry with no data forthcoming and in the interest of completing this research, a decision was made to randomly select a different state from the Midwest region, Nebraska was selected. Table 10: Final NAACCR Eight State Cancer Registries REGION

WEST SOUTH

MIDWEST NORTHEAST

STATE

2000

2001

2002

2003

2004

Annual Incidence

Average Annual Cases

OREGON

gold

gold

gold

gold

gold

69

2489

IDAHO

gold

gold

gold

gold

gold

55.2

683

FLORIDA SOUTH CAROLINA

gold

gold

gold

gold

gold

74.6

15838

silver

silver

gold

gold

gold

74.7

3111

INDIANA

silver

gold

gold

silver

gold

80.6

4931

NEBRASKA

gold

silver

gold

gold

gold

78.6

4731

MASSACHUSETTS

gold

gold

gold

gold

gold

70.5

4764

RHODE ISLAND

gold

gold

gold

gold

gold

73.8

860

In summary, the selection criterion for this research utilized a process to minimize selection bias. For example, Criterion 4, the state registry must use passive reporting

101

methods as outlined by NAACCR and not use other methods for reporting as in SEER State registries. Possible geographic differences in the population under study are addressed with selection criterion 3. It is critical to get a fair comparison of lung cancer cases; selecting cases just from the Northeast could introduce bias possibly invalidating several study results. The 10th item for selecting a state (registry) is the selection cannot be done by the researcher in a biased manner; the selection must be made by a random assignment. The last criterion, Item 11, (selection of the state based cancer registry) is that the data must be available for acquisition. If the data cannot be acquired from a state registry by the author that state cancer registry will be excluded from selection. The seventh criterion is that the state registry must be a member of NAACCR. The NAACCR has standardized guidelines for abstracting data. Data can only be compared if the methods and information collected are consistent and complete. Deviations from a standardized format can introduce error into the study results affecting internal validity and external validity.

Variables of Interest (Inclusion and Exclusion Criteria) Inclusion Criteria Primary lung cancer cases from state population based cancer registries that are a member of the North American Association of Central Cancer Registries were identified. A primary site classification (NAACCR Code 400) is made by the state cancer registry based on site of tumor origin and specified in the medical record. In this research the

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primary site code is specified by ―Code 36‖ for lung. This classification is made in accordance with ICD-O coding schemes. Cases that were diagnosed between January 1, 2000 and December 31, 2004 were selected. An extensive inclusion criteria are listed in this Chapter (Three) under the section ―Variables of Interest‖; some of the inclusion criteria include a known date of diagnosis, that the case must be confirmed from a tissue or cell sample and in terms of the case assessment: the primary lung cancer case was classified as analytic 1, 258. In order to perform survival analysis, it is critical to have an origin or beginning (date of diagnosis will serve as the origin), an observed time range, and an endpoint/conclusion for the study or a valid analysis cannot be completed. Also, for this research, a lung cancer case must be diagnostically confirmed by means of a positive histology from the tumor tissue or a positive cytology (cells examined microscopically) as not to bias any subsequent results with the addition of histologically unconfirmed primary lung cancer cases in the data set. The descriptions of ―Diagnostic Confirmation‖ codes are listed in Table 3.10.; those codes include 1, 2, 4, 5 - the codes describe the methods of a diagnostic technique with a lung tissue/cell sample of the tumor. An analytic lung cancer case classification code denotes that part of the diagnosis and/or treatment of the lung cancer case was performed at the reporting (cancer registry) facility. An analytic case can further be defined under the NAACCR classification variable known as the ―Class of Case‖. Class of Case (NAACCR Code 610) describes the criterion for inclusion as an analytic case with the codes 0-2 as shown in Table 3.12. Non-analytic cases (codes 3 through 9) are cases that can have a greater chance of error

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as the information is abstracted from case information not directly associated with the reporting facility. The data from non-analytic lung cancer cases can be subject to bias when the information is provided by a patient (recall bias) that had the diagnosis and treatment at a facility different from the reporting facility. The decision was made to include only analytic cases for this research.

Exclusion Criteria As only primary lung cancer cases are included in this study, secondary or recurrent lung cancer case will be excluded. The variables of interest such as gender include codes or categories that are not of interest in this research; those data are excluded. These gender codes include 3= hermaphrodite, 4 = transsexual, or 9 = unknown/not stated 258, 263. Other variable codes of interest that have a code category not known or not stated (code = 9) must be identified during exploratory analysis. The number of missing values (codes for the sex of an individual cancer case) can possibly impact the research results. These values can provide information and knowing the exact number of individuals not represented in the final data set is important as it can decrease the validity of the results (increases the uncertainty) if the number of missing values is large. Some of the other variables that include the not know or not stated category (code = 9) are primary site, histology, stage, grade, treatment type (chemotherapy, surgery, radiation therapy), tobacco use, marital status, and vital status. It was important to address the missing values and record the number for each category as to assess the

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impact those missing values on the study results. In the next section ―Variable Identification and Coding‖ (Chapter Three), each of the research patient parameters or variables of interest will be described. The central cancer registry information/data variables that were requested by the investigator for each state cancer registry are listed in Table 11.

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Table 11: NAACCR Variable Code and Description NAACCR Code

NAACCR Code

Description of Variable

Description of Variable

1

20

Patient ID Number

24

480

Morphology Coding System - Original

2

150

Marital Status at DX

25

490

Diagnostic Confirmation

3

160

Race 1

26

500

Type of Reporting Source

4

161

Race 2

27

560

Sequence Number - Hospital

5

162

Race 3

28

580

Date of 1st Contact

6

163

Race 4

29

610

Class of Case

7

164

Race 5

30

630

Primary Payor at DX

8

190

Spanish/Hispanic Origin

31

759

SEER Summary Stage 2000

9

220

Sex

32

760

10

230

Age at Diagnosis

33

1200

11

240

Birth Date

34

1210

12

250

Birthplace

35

1220

13

340

Tobacco History

36

1290

14

390

Date of Diagnosis

37

1360

15

400

Primary Site

38

1390

SEER Summary Stage 1977 Record Date of First Surgery Record Date of First Radiation Record Date of First Chemotherapy Record Summary Surgical Primary Site Record Summary Radiation Record Summary Chemotherapy

16

410

39

1750

Date of Last Contact

17

419

Laterality Morphology Type and Behavior ICD-O-2

40

1760

Vital Status

18

420

Histology (92-00) ICD-O-2

41

1910

Cause of Death

19

430

42

1930

Autopsy

20

521

Behavior (92-00) ICD-O-2 Morphology Type and Behavior ICD-O-3

43

1940

Place of Death

21

522

Histology (92-00) ICD-O-3

44

2090

Date Case Completed

22

523

Behavior (92-00) ICD-O-3

45

2110

23

440

Grade

46

3000

Date Case Exported Derived AJCC Stage Summary

Some of the research variables (independent or explanatory and dependent or 106

outcome) include the individual lung cancer case identifier assigned by the cancer registry (this number was de-identified to the researcher of this study), the cancer registry identification number, the date of first contact (the date may be representative of a physician’s visit, biopsy, x-ray, or laboratory test), the treatment modality received (radiation, surgery, chemotherapy), and the type of reporting source, e.g. hospital, outpatient facility. It should be noted that the date of lung cancer diagnosis, NAACCR recommends that the best approximation for the date of diagnosis should be used versus coding the date as unknown (9) 258. Therefore in the situation of the year of diagnosis being known but no other information on the month or day is given, the general abstracting instruction is to use June 15 for the year indicated. If the year and month are available but not the day, the 15th of the month is entered. The other patient demographic variables of interest include gender, race, marital status at time of diagnosis, primary insurance payer at diagnosis, a birthplace Geocode, and birth date. Table 11 includes NAACCR variable names and the associated NAACCR item number to that variable. Table 11 provides the complete list of patient or individual lung cancer case information that was intended to be utilized in this study. Tumor information or data are collected to describe the cancer case; this descriptive information includes the tumor histology, tumor type and stage, date of diagnosis, and how the diagnosis was made. The individual lung cancer case data that identifies dates that can be used for an origin or beginning of the study time period and stop or end date are required for survival analysis. All variables such as the date of diagnosis, vital status (alive or dead), date of last contact or date of

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death, were used to answer the research questions as it pertains to survival in conjunction with gender, treatment type, and/or tumor descriptor variables.

Variable Identification and Coding Patient Identification (ID) Number NAACCR Designated Item Number = 20 The identification number is a unique NAACCR number assigned to a particular individual (patient). The ID number serves several functions and purposes as a unique identifier for a particular individual. It is the recommendation of NAACCR, that a previously assigned patient ID number is never reused or reissued if a patient file is deleted. This number will follow the patient assigned a unique Patient Identification (ID) Number. Different state cancer registries may report tumor information on the same patient to NAACCR or commonly referred to as the central registry. In this instance, the central registry will identify that individual, verify any duplicate records, and then assign a unique patient ID number, exclusive to that patient. This number assignment serves to follow the individual patient throughout his/her cancer history regardless of any subsequent tumors that are reported for the patient. Marital Status at Diagnosis (DX) NAACCR Designated Item Number = 150 When the tumor information is reported, the patient’s marital status on the diagnosis date is recorded. The martial status can be different depending upon the tumor being reported, as an individual may have different tumor sites. This variable is important as the incidence and survival has been shown to vary by marital status with 108

particular cancer types 264, 265 . The codes used by the central registry for marital status are 1) Single (never married), 2) Married (including common law), 3) Separated, 4) Divorced, 5) Widowed, 9) Unknown. Race 1 NAACCR Designated Item Number = 160 The Race coding used by NAACCR is taken from the 2000 US Census Bureau definitions for race, see Table 12 below. Table 12: NAACCR Code and Description of Race Code 01 02 03

Code 20 21

NAACCR Designation Micronesian, NOS Chamorran

22

Guamanian, NOS

04 05 06 07 08 09 10 11

NAACCR Designation White Black American Indian, Aleutian, or Eskimo (includes all indigenous populations of the Western hemisphere) Chinese Japanese Filipino Hawaiian Korean Asian Indian, Pakistani Vietnamese Laotian

25 26 27 28 30 31 32 96

12 13 14 15

Hmong Kampuchean Thai Micronesian, NOS

97 98 99

Polynesian, NOS Tahitian Samoan Tongan Melanesian, NOS Fiji Islander New Guinean Other Asian, including Asian, NOS and Oriental, NOS Pacific Islander, NOS Other Unknown

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Race 2 NAACCR Designated Item Number = 161 109

When an individual is multiracial, Race 2 through Race 5 is coded and prior to 2000 Race 2 through 5 was blank. The acceptable coding for each race designation is shown in Table 12 under Race 1. Race 3 NAACCR Designated Item Number = 162 Race 4 NAACCR Designated Item Number = 163 Race 5 NAACCR Designated Item Number = 164 Spanish/Hispanic Origin NAACCR Designated Item Number = 190 Spanish or Hispanic origin does not use the same code as Race 1 through Race 5. Origin as defined by the Census Bureau is the ―heritage, national group, lineage, or country of birth of the person or the person’s parents or ancestors before their arrival in the United States. The NAACCR Coding Manual states that people who identify their origin as Spanish, Hispanic, or Latino, may be of any race 266. This particular item variable was used in an attempt by the US Census Bureau to increase the reporting accuracy of the data.

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Table 13: NAACCR Code and Description of Spanish/Hispanic Origin Code 0 1 2 3 4 5 6 7 8 9

Description of Spanish/Hispanic Origin Non-Spanish; non-Hispanic Mexican (includes Chicano) Puerto Rican Cuban South or Central American (except Brazil) Other specified Spanish/Hispanic origin (includes European; excludes Dominican Republic) Spanish, NOS Hispanic, NOS Latino, NOS 'unknown whether Spanish or not' should be used

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Sex NAACCR Designated Item Number = 220 This variable is one of the key variables under study for gender differences research. Under the coding scheme of NAACCR, there are 5 codes for this classification: 1) 1 = Male, 2) 2 = Female 3) 3 = Other (Hermaphrodite), 4) 4 = Transsexual and 5) 9 = Not Stated or Unknown. Age at Diagnosis NAACCR Designated Item Number = 230 The patient’s age at the time of tumor diagnosis is recorded in years. The coding scheme is shown in actual years of age, e.g. a 57 year old would be coded as 057. Other examples of age coding are given as 000 for less than 1 year old, 001 for 1 year old, but less than 2 years, 002 represents 2 years of age, 101 for 101 years, 120 for 120 years old, and 999 for an unknown age. Birth Date 111

NAACCR Designated Item Number = 240 The NAACCR format is given as MMDDCCYY, where MM is the month (01 12), DD the day (01 – 31) and CCYY, the year. The birth date is coded in an 8 character format as either a valid date in the NAACCR format or 99999999 (8 characters) if unknown. The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary recommends that if the birth year is unknown, the birth date can be calculated from the age of diagnosis and the year of diagnosis. The coding for the month and the day would be 9999 as unknown but the calculated year would be used. The NAACCR Standards further state an estimated birth date is better than an unknown value. Birthplace NAACCR Designated Item Number = 250 The coding of the birthplace of an individual is found in the SEER Program Code Manual Appendix B. This variable is of interest as variations in disease patterns, genetic and socioeconomic characteristics have been demonstrated in the literature varying on place of birth 82, 261, 267. Tobacco History NAACCR Designated Item Number =340 NAACCR does not have a designated code for tobacco use or tobacco history. Coding schemes for tobacco use varies across state cancer registries. Date of Diagnosis NAACCR Designated Item Number = 390

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The date of diagnosis is the initial date the primary lung tumor was identified. The coding of the date is done with the same ―date‖ format as the variable, Birth Date (240). Primary Site NAACCR Designated Item Number = 400 The primary tumor site for this particular research is 36, lung. The coding used by NAACCR is designated by the International Classification of Disease – Oncology. Laterality NAACCR Designated Item Number = 410 Laterality is used for paired organs and describes which lung (left or right) has been diagnosed with the primary tumor (Table 14). Table 14: NAACCR Code and Description of Laterality Code 0 1 2 3 4 9

Description of Laterality Not a paired site Right: origin of primary Left: origin of primary Only one side involved, right or left origin unspecified Bilateral involvement, lateral origin unknown; stated to be single primary; including both ovaries Paired site, but no information concerning laterality, midline tumor

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Morphology Type and Behavior ICD-O-2 NAACCR Designated Item Number = 420 The morphology code is representative of the cell type and the biological activity the tumor presents. The ICD-O coding system uses a morphology code based on the histology (cell type), behavior code, and grade; the codes are given in Table 15. The first

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four digits of the morphology code denote the cell histology, the fifth digit is the behavior and the last digit is the grade. The ICD-O-2/3 terms include 1) C34.0 for the main bronchus, 2) C34.1 for the upper lobe of the lung, 3) C34.2 for the middle lung lobe (this can be for the right lung only), 4) C34.3 lower lobe, lung, 5) C34.8 an overlapping lesion of lung, and 6) C34.9 Lung, NOS. The histologic and behavior codes vary as a function of the lung cancer type. Table 15: NAACCR Code and Description of LC Morphology

Lung Cancer Type Small Cell Lung Cancers

Squamous or Epidermoid Adenocarcinoma Bronchi alveolar

ICD-O Morphology Codes 80413 80423 80433 80443, 80453 807_3

Lung Cancer Type Large Cell Carcinoma

Adenosquamous Carcinoma Non-Small Cell Carcinoma

814_3

ICD-O Morphology Codes 80123

85603 80463

82503

Source: Florida Cancer Data System November 2003 Monthly Memo

Histology (92-00) ICD-O-2 NAACCR Designated Item Number = 420 There are three parts of the coding scheme for the morphology code and the tumor type is classified under ―histology‖ which is the first part (first 4 digits) of the morphology code. A complete explanation of the history classification scheme was given in Chapter Two under the Pathology/Histology section. As shown in Table 15, the first four digits are representative of the coding used for lung cancer cases. Each lung cancer

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type is classified and coded by standardized methods, as discussed in Chapter Two. Behavior (92-00) ICD-O-2 NAACCR Designated Item Number = 430 The behavior of a tumor is the way or the mode of tumor growth or progression within the human body. The physician, normally a pathologist, observes the tumor behavior and classifies the growth pattern. It would be important to select or include cases with the behavior code of ―3‖ (see Table 16.) Table 16: NAACCR Code and Description of LC Behavior Code /0 /1 /2 /3 /6 /9

Description of Behavior Benign Uncertain whether benign or malignant, borderline malignancy, low malignant potential, uncertain malignant potential Carcinoma in situ, intraepithelial, non-infiltrating, non-invasive Malignant, primary site Malignant, metastatic or secondary site Uncertain whether primary or metastatic site

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Morphology Type and Behavior ICD-O-3 NAACCR Designated Item Number = 521 Coding for the lung cancer type or morphology essentially did not change from ICD-O-2 to ICD-O-3; for other tumor types and disease classifications, the ICD-O coding did change. As part of the quality assurance procedure, ICD-O-2 lung cancer cases will be compared to the ICD-O-3 cases, to ensure that the data are consistent. Another method to identify any errors or errors in duplication will be to utilize the NAACCR variable (code 480) ―Morphology Coding System – Original‖. There should be

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consistency between Morphology Type and Behavior ICD-O-2, Morphology Type and Behavior ICD-O-, and Morphology Coding System – Original. Histology (92-00) ICD-O-3 NAACCR Designated Item Number = 522 ICD-O-3 Histology or cell/tumor type designation is the same coding scheme as Histology ICD-O-2. Behavior (92-00) ICD-O-3 NAACCR Designated Item Number = 522 The ICD-O-3 Behavior or cell growth pattern designation is the same coding scheme as described in Behavior IDC-O-2. Grade NAACCR Designated Item Number = 440 The code for grade describes the cells of the tumor. Grades I through IV (codes 1 – 4 as shown in Table 17) are utilized and the other grades listed in that table below are not applicable to this research.

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Table 17: NAACCR Code and Description for Grade Code

Description of Grade

1

Grade I

2

Grade II

3

Grade III

4

Grade IV

Equivalent Term* Grade I; grade 1; Well differentiated; Differentiated, NOS Grade II; grade 2; Moderately differentiated; Moderately well differentiated; Intermediate differentiation; Low grade; Partially well differentiated; Relatively well differentiated; Generally well differentiated; Fairly well differentiated; Intermediate differentiation; Grade I of 3 category system; Grade I-II; Trabecular Grade III, grade 3; Poorly differentiated; Dedifferentiated; Medium grade; Moderately undifferentiated; Relatively undifferentiated; Relatively poorly differentiated; Grade II of 3 category system; Grade II-III Grade IV; grade 4; Undifferentiated; Anaplastic; High grade; Grade III of 3 category system; Grade III-III

5 6 7 8 9

T-cell B-cell Null cell NK (natural killer) cell Grade/differentiation Cell type not determined, not stated or not applicable; unknown, not stated, or No grade/differentiation in the primary site even if a not applicable grade is given for a metastatic site. Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007 *Source: Florida Cancer Data System Data Acquisition Manual 2006

Morphology Coding System – Original NAACCR Designated Item Number = 480 The morphology coding system originally used will be utilized as a second check to ensure data quality in the reporting and data received from the cancer registries.

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Diagnostic Confirmation NAACCR Designated Item Number = 490 Diagnostic confirmation is essential for verification of a particular tumor type. This confirmation can be made utilizing various methods such as an examination by a pathologist or cytologist of tissues or cells via the microscope. For this research, certain criteria for the variables of interest must be used when evaluating a particular tumor type, in particular, in the study of lung cancer. One criterion to know is the specific scientific method used to diagnosis the tumor type. Biological confirmation of the cancer type is the gold standard in the medical community. In the NAACCR coding scheme, other diagnostic confirmation methods other than biologic confirmation such as direct visualization of the tumor are included and are shown in Table 18. The codes for the reporting method for confirming a particular tumor type in this research will only include biologically confirmed methods: 1) positive histology, 2) positive cytology, no positive histology, 4) positive microscopic confirmation, method not specified, and 5) Positive laboratory test/marker study as shown in Table 18.

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Table 18: NAACCR Code and Description Diagnostic Confirmation Code

Description of Diagnostic Confirmation

1 2 4 5 6

Positive histology Positive cytology, no positive histology Positive microscopic confirmation, method not specified Positive laboratory test/marker study Direct visualization without microscopic confirmation Radiography and other imaging techniques without microscopic confirmation Clinical diagnosis only (other than 5, 6, or 7) Unknown whether or not microscopically confirmed

7 8 9

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Type of Reporting Source NAACCR Designated Item Number = 500 The tumor information is contained in many different records at different facilities. As the data are abstracted or collected in a standardized manner, it is necessary to identify where the information was obtained from. As an example, information from laboratory reports identified from the medical record in a medical oncology center would have the reporting center coded as 2 – see Table 19. It is well documented in the literature 57, 229 that death certificates many have incomplete information and may not represent a complete picture of the patient’s medical history. It would be important to the investigator to be aware that reporting source with a code of 7 as to address any discrepancies during the analysis.

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Table 19: NAACCR Code and Description for Reporting Source Type Code 1 2 3 4 5 6 7 8

Description of Type of Reporting Source Hospital inpatient; Managed health plans with comprehensive, unified medical records Radiation Treatment Centers or Medical Oncology Centers (hospital-affiliated or independent) Laboratory only (hospital-affiliated or independent) Physician's office/private medical practitioner (LMD) Nursing/convalescent home/hospice Autopsy only Death certificate only Other hospital outpatient units/surgery centers

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Sequence Number NAACCR Designated Item Number = 560 This code is used by the cancer registry to identify primary, secondary, or multiple lung tumors. As this research is focused on primary lung cancer cases, this designation is 00 and codes other than the 00 will be identified. Date of Admission or First (Adm/1st) Contact NAACCR Designated Item Number = 580 This variable designates the date the first time a case was contacted either as an outpatient or inpatient. The coding of the date is done with the same ―date‖ format as the variable, Birth Date (240). The date may be representative of an outpatient procedure, an x-ray, or pathology report associated with the diagnosis of the tumor. Class of Case NAACCR Designated Item Number = 610 Class of case describes the location of the reporting facility where the diagnosis 120

was made and the codes for Class of Case are described in Table 20. Analytic cases are those that are diagnosed at the reporting facility and include the codes 0 through 2. The other codes include nonanalytic cases that are identified at the reporting facility but were diagnosed and treated at a different facility. These cases also include those diagnosed at autopsy. Table 20: NAACCR Code and Description for Class of Case Codes

Description of Class of Case

Analytic Cases 0 Diagnosis at the reporting facility and the entire first course of treatment was performed elsewhere or the decision not to treat was made at another facility. 1 Diagnosis at the reporting facility, and all or part of the first course of treatment was performed at the reporting facility. 2 Diagnosis elsewhere, and all or part of the first course of treatment was performed at the reporting facility. Non-analytic Cases 3 Diagnosis and the entire first course of treatment were performed elsewhere. Presents at your facility with recurrence or persistent disease. 4 Diagnosis and/or first course of treatment were performed at the reporting facility prior to the reference date of the registry. 5 Diagnosed at autopsy 6 Diagnosis and the entire first course of treatment were completed by the same staff physician in an office setting. ―Staff physician‖ is any medical staff with admitting privileges at the reporting facility. 7 Pathology report only. Patient does not enter the reporting facility at any time for diagnosis or treatment. This category excludes tumors diagnosed at autopsy. 8 Diagnosis was established by death certificate only. Used by central registries only. 9 Unknown. Sufficient detail for determining Class of Case is not stated in patient record. Used by central registries only. Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

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Primary Payor at Diagnosis NAACCR Designated Item Number = 630 Information about the insurance carrier at the time of diagnosis can be an important variable. Possible treatment disparities between minority groups with lung cancer and the association between the types of insurance coverage could be of interest. Table 21 lists the codes that are associated with the particular payor at the time of lung cancer diagnosis.

Table 21: NAACCR Code and Description for Payor at Diagnosis Code 01 02 10 20 21 31 35 60 61 62 63 64 65 66 67 68 99

Description of Primary Payor at Diagnosis Not insured Not insured, self-pay Insurance, NOS Private Insurance: Managed care, HMO, or PPO Private Insurance: Fee-for-Service Medicaid Medicaid -Administered through a Managed Care plan Medicare/Medicare, NOS Medicare with supplement, NOS Medicare - Administered through a Managed Care plan Medicare with private supplement Medicare with Medicaid eligibility TRICARE Military Veterans Affairs Indian/Public Health Service Insurance status unknown

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

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SEER Summary Stage 1977 NAACCR Designated Item Number = 760 The coding scheme for SEER Summary Stage 1977 (Table 22) is used at the time of initial primary tumor diagnosis. Stage was a variable that was historically monitored for time trends. Table 22: NAACCR Code and Description SEER Summary Stage 1977 Codes 0 1 2 3 4 5 7 8 9

Description of SEER Summary Stage 1977 In situ Localized Regional, direct extension only Regional, regional lymph nodes only Regional, direct extension and regional lymph nodes Regional, NOS Distant Not applicable Unstaged

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

SEER Summary Stage 2000 NAACCR Designated Item Number = 2000 SEER Summary Stage 2000 at initial diagnosis is a variable that includes the description of the reportable tumor. Table 23 exhibits the site-specific single-digit coding scheme explicit to the tumor location.

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Table 23: NAACCR Code and Description SEER Summary Stage 2000 Codes 0 1 2 3 4 5 7 8 9

Description of SEER Summary Stage 2000 In situ Localized Regional, direct extension only Regional, regional lymph nodes only Regional, direct extension and regional lymph nodes Regional, NOS Distant Not applicable Unstaged

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Record (RX) Date of First Surgery NAACCR Designated Item Number = 1200 The date of the first surgery for the primary tumor is coded with the NAACCR format given as MMDDCCYY, where MM is the month (01 - 12), DD the day (01 – 31) and CCYY, the year. The surgical date is coded in an 8 character format as either a valid date in the NAACCR format or 99999999 (8 characters) if it is unknown if any surgical procedure was performed. If there was no surgical procedure performed or if the individual was an autopsy-only case, the code would be 00000000. Record (RX) Date of First Radiation NAACCR Designated Item Number = 1210 This is the date that the treatment modality, radiation therapy began at any radiation therapy facility, e.g. hospital, outpatient center, for the patient’s first course of their treatment. The coding for this variable is the NAACCR format MMDDCCYY, where MM is the month (01 - 12), DD the day (01 – 31) and CCYY is the year. Other

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variations include 00000000 when radiation therapy is not administered; autopsy-only case, 88888888 if radiation therapy was scheduled as part of the first course of therapy, but was not started at the time and 99999999 if the date was unknown, it was unknown whether any radiation therapy was administered; or if the case was only identified by death certificate. Record (RX) Date of First Chemotherapy NAACCR Designated Item Number = 1220 This designation is the date that chemotherapy was first started. The format used by NAACCR is the same as in ―Birth Date‖. The other codes admissible include 00000000 when chemotherapy is not administered or in the case of an autopsy. Record (RX) Summary of Surgery for Primary Site NAACCR Designated Item Number = 1290 The summary of the surgery performed for the primary tumor site is given below in Table 24. As the disease of interest for this research is lung cancer, all surgical sites will be specific to regions of the lung.

Table 24: NAACCR Code and Description of Surgical Primary Site Code 00 10-19 20-80 90 98 99

Description of Record (RX) Summary of Surgery for Primary Site None Site-specific code; tumor destruction Site-specific codes; resection Surgery, NOS Site specific codes; special Unknown

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

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Record (RX) Summary of Radiation NAACCR Designated Item Number = 1360 The coding for ―Record Summary of Radiation‖ described in Table 25 with an explanation the type of radiation treatment the lung cancer case received.

Table 25: NAACCR Code and Description of Radiation Treatment Code 0 1 2 3 4 5 6 7 8 9

Description of Radiation Treatment None Beam radiation Radioactive implants Radioisotopes Combination of 1 with 2 or 3 Radiation, NOS—method or source not specified Currently allowable for historic cases only; see Note below Patient or patient’s guardian refused* Radiation recommended, unknown if administered* Unknown if radiation administered

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007 “Note: In the SEER program, a code 2 for other radiation was used between 1973 and 1987. When the radiation codes were expanded to add codes '2' radioactive implants and '3' radioisotopes, all cases with a code '2' and diagnosed in 1973-1987 were converted to a code '6' radiation other than beam radiation.‖

Record (RX) Summary of Chemotherapy NAACCR Designated Item Number = 1390 The chemotherapy codes used for the NAACCR Designated (Item Number = 1390) Record Summary of Chemotherapy are listed in Table 26. The code is specified when a chemotherapy agent/drug is received or not administered to an individual case as part of the first treatment for lung cancer. Also a code is given to identify when it is unknown if the lung cancer case received chemotherapy, i.e. codes 88 and 99.

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Table 26: NAACCR Code and Description for Chemotherapy Code 00 01 02 03 82

85 86

87

88 99

Description of Chemotherapy Treatment None, chemotherapy was not part of the planned first course of therapy. Chemotherapy, NOS Chemotherapy, single agent. Chemotherapy, multiple agents. Chemotherapy was not recommended nor administered because it was contraindicated due to patient risk factors, i.e., comorbid conditions, advanced age. Chemotherapy was not administered because the patient died prior to planned or recommended therapy. Chemotherapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record. Chemotherapy was not administered; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record. Chemotherapy was recommended, but it is unknown if it was administered. It is unknown whether a chemotherapeutic agent(s) was recommended or administered because it is not stated in patient record; death certificate-only cases.

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Derived AJCC Stage Group NAACCR Designated Item Number =3000 This variable, Derived AJCC Stage Group, encompasses all stage designations from the AJCC Sixth Edition TNM stage, SEER Summary Stage 1977, and SEER Summary Stage 2000 and complies the different coding into this one item number, 3000. The coding designation, shown in Table 27 came into effect as a result of a joint task force so a common, uniform set of rules and coding will be available. Representatives from SEER, ACoS, CDC, NAACCR, NCRA, and AJCC collaborated on the coding designation to standardize the grouping of disease stage. 127

Table 27: Derived AJCC Stage Group AJCC Code 00 01 02 10 11 12 13 14 15 16 17 18 19 23 24 20 21 22 30 31 32 33 34 35 36 37 38 39 40 41 42 43 50 51 52 53 54 55

Display String 0 0a 0is I INOS IA IA1 IA2 IB IB1 IB2 IC IS ISA ISB IEA IEB IE II IINOS IIA IIB IIC IIEA IIEB IIE IISA IISB IIS IIESA IIESB IIES III IIINOS IIIA IIIB IIIC IIIEA

Comments Stage 0 Stage 0a Stage 0is Stage I Stage I NOS Stage IA Stage IA1 Stage IA2 Stage IB Stage IB1 Stage IB2 Stage IC Stage IS Stage ISA (lymphoma only) Stage ISB (lymphoma only) Stage IEA (lymphoma only) Stage IEB (lymphoma only) Stage IE (lymphoma only) Stage II Stage II NOS Stage IIA Stage IIB Stage IIC Stage IIEA (lymphoma only) Stage IIEB (lymphoma only) Stage IIE (lymphoma only) Stage IISA (lymphoma only) Stage IISB (lymphoma only) Stage IIS (lymphoma only) Stage IIESA (lymphoma only) Stage IIESB (lymphoma only) Stage IIES (lymphoma only) Stage III Stage III NOS Stage IIIA Stage IIIB Stage IIIC Stage IIIEA (lymphoma only) 128

56 57 AJCC Code 58 59 60 61 62 63 70 71 72 73 74 88 90 99

IIIEB IIIE Display String IIISA IIISB IIIS IIIESA IIIESB IIIES IV IVNOS IVA IVB IVC NA OCCULT UNK

Stage IIIEB (lymphoma only) Stage IIIE (lymphoma only) Comments Stage IIISA (lymphoma only) Stage IIISB (lymphoma only) Stage IIIS (lymphoma only) Stage IIIESA (lymphoma only) Stage IIIESB (lymphoma only) Stage IIIES (lymphoma only) Stage IV Stage IV NOS Stage IVA Stage IVB Stage IVC Not applicable Stage Occult Stage Unknown

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Date Case Report Received NAACCR Designated Item Number = 2111 The ―Date Case Report Received‖ is the date that the source record is submitted and received by the central cancer registry 258. In the event of multiple reports on the same individual and one date is needed, the protocol is to use the first date the record was received. This variable can be used to evaluate the reporting timeliness of the cancer registries. This variable may also be used to measure how long the individual cancer registry takes to submit the data when the date the report (2111) is received is compared to the date of first contact (580).

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Date of Last Contact NAACCR Designated Item Number =1750 The Date of Last Contact is the last date of known contact but also can represent the date of death. The date is obtained from either an active or a passive follow-up. The sources include the state registries date of last contact or death (passive) or from an active state SEER registry or the National Death Index. The date coding follows the NAACCR format of MMDDCCYY, where MM is the month (01 - 12), DD the day (01 – 31) and CCYY is the year. The main purpose of this variable is to record the date of last contact or date of death. Vital Status NAACCR Designated Item Number =1760 The vital status of the individual as given by the NAACCR is 0 for dead, 1 for alive, 4 for dead. A code of 0 is obtained from states that report based on the guidelines of the Commission on Cancer (passive registry) and a code of 4 is based on information obtained from a SEER state (active registry). Follow-Up Source NAACCR Designated Item Number =1790 The source is given for the most currently recorded information for an individual. Any discrepancies in the record can be reviewed and cross-checked with other variable coding. Table 28 lists the sources or the contributors to the follow-up data. It includes information reported from the Department of Motor Vehicles, death certificate information, patient or physician reporting, Medicare/Medicaid files, and if the data is unknown, not stated in the patient record. 130

Table 28: NAACCR Code and Description of Follow-Up Sources Code 0 1 2 3 4 5 7 8 9

Description of Follow-Up Source Reported hospitalization Readmission Physician Patient Department of Motor Vehicles Medicare/Medicaid file Death certificate Other Unknown, not stated in patient record

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Autopsy NAACCR Designated Item Number =1930 This designation is the indicator if an autopsy was performed or not. This information could be use to verify the correctness of the coding for other variables. For example, the coding for this NAACCR designated item number, 1930, could be compared to a patient status code for a particular individual to check for agreement. A case could not be alive if an autopsy was performed.

Table 29: NAACCR Code and Description of Autopsy Code 0 1 2 9

Description of Autopsy Not applicable; patient alive Autopsy performed No autopsy performed Patient expired, unknown if autopsy performed

Source: The North American Association of Central Cancer Registries Standards for Cancer Registries Volume II Data Standards and Data Dictionary 2007

Place of Death NAACCR Designated Item Number = 1940 131

This variable is useful to correlate to the date of last contact. If the patient is coded as alive and a place of death is documented, further investigation is warranted. Date Case Completed NAACCR Designated Item Number = 2090 The date can be used to assess the quality and timeliness of reporting for the data. Date Case Exported NAACCR Designated Item Number = 2110 The date can be used to assess the quality and timeliness of reporting for the data. Derived AJCC Stage Summary NAACCR Designated Item Number = 3000 This variable is used to compare stage information.

Epidemiologic Research Design The epidemiologic study design for this research was based on a historical cohort of primary lung cancer case. Female lung cancer cases were compared to male lung cancer cases and this comparison between genders included the histological type, stage, and grade of lung cancer and the treatment received (chemotherapy, radiation therapy, surgery, or combination) as variables of interest. Some of the strengths of this study design are multiple effects of the exposure were assessed simultaneously. Historically, a weakness of a retrospective or historical study design is it can be prone to bias due to recall or information bias. This particular limitation or weakness was minimized as the data were collected by a standardized, controlled method utilizing trained cancer registry abstractors; information was provided by medical records and non-analytic cases were 132

excluded, e.g. information provided by the patient or members of the family. As the majority of the states are mandated by law to report cancer case information; noncompliance is minimal and monitored by the NAACCR; therefore the case information is assumed to be complete.

Data Collection Methods Prior to the collection of any lung cancer case information, approval from the University of South Florida Internal Review Board Data (IRB) was sought by the principle investigator, PI. The chief concern of the IRB was that the case information could not be used to identify any one particular individual. The research data acquired for primary lung cancer cases for this study are cases from state cancer registries that are members of NAACCR. Initially, there were two main approaches to collecting the lung cancer case information. The first method was to contact the eight states selected (Table 10). As stated previously, West Virginia was randomly selected but the cancer registry was unable to comply with the data research request. Table 30 outlines the contact process followed for the cancer registries and the contact information. The data set acquisitions was done in accordance to protocol and procedures outlined by each state based cancer registry.

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Table 30: State Cancer Registry Contact Information

State Oregon

Idaho

Florida

South Carolina

Indiana

Massachusetts

Nebraska

Rhode Island

Contact Information

Requirements for the Release of Data Requested Lung Cancer Data and sent the following three files: 1. The PDF file of the approval letter from the IRB at the University of South Florida concerning my dissertation research. 2. The variables of interests are outlined in "NAACCR VARIABLE" (the data will be utilized as part of my dissertation/research project). 3. The eight state cancer registries selected to participate in my dissertation research project that includes Oregon.

Catherine Riddell (great resource) Research Analyst Oregon State Cancer Registry Phone: (971) 673-1113 FAX: (971) 673-0996 [email protected]

Cancer Data Registry of Idaho 615 N. 7th Street P.O. Box 1278 Boise, Idaho 83701 http://www.idcancer.org/generalinfo.html

The Cancer Data Registry of Idaho has a release requirement and form that must be submitted prior to the release of any data.

Florida Cancer Data System http://fcds.med.miami.edu/

The FCDS has a release requirement and form that must be submitted prior to the release of any data. The data request forms are located on the FCDS website at: http://fcds.med.miami.edu/inc/datarequest.shtml

S.C. Department of Health & Environmental Control S.C. Central Cancer Registry 810 Dutch Square Blvd., Ste. 220 Columbia, SC 29210

The South Carolina Central Cancer Registry has a release requirement and form that must be submitted prior to the release of any data. Telephone # (803) 731-1419 Fax # (803) 731-1455

Indiana State Department of Health Epidemiology Resource Center 2 North Meridian, 5K Indianapolis, IN 46204 317-233-7807 317-234-2812 FAX

The Indiana State Department of Health has a release requirement and form that must be submitted prior to the release of any data.

Massachusetts Cancer Registry 250 Washington Street, 6th Floor Boston, MA 02108 Phone: (617) 624-5642 Fax: (617) 624-5695 Annie McMillan (great resource and extremely helpful) Nebraska Cancer Registry Nebraska Comprehensive Cancer Control Program Janis Singleton (very nice) 3 Capitol Hill Providence, RI 02908 (401)222-1172 Fax: 222-3551 http://www.health.ri.gov/disease/cancer/regis try.php

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The Massachusetts Cancer Registry has a release requirement and form that must be submitted prior to the release of any data. Confidential Data Officer Privacy and Data Access Office Massachusetts Department of Public Health 250 Washington Street, 2nd Floor Boston, MA 02108-4619 TEL: (617) 624-5229 FAX: (617) 624-5234 IRB approval process DHHS, Division of Public Health 301 Centennial Mall South Lincoln NE 68509 The Rhode Island Cancer Registry has a release requirement and form that must be submitted prior to the release of any data. Main Contact (extremely helpful): John P. Fulton, PhD, RI Department of Health: 401-277-1394 x115

The other or second method of data case acquisition was attempted through the central cancer registry, NAACCR, for the states of interest (Table 9). To answer the research questions, data from the eight states was required. It was considered a viable option that collecting the data from a centralized data bank, like NAACCR, would streamline the data collection process. The second option was pursued via multiple data requests made directly to NAACCR. The complete approval/disapproval process for data release took over a year and after multiple requests and multiple re-submissions for the lung cancer data, NAACCR determined they would provide the information to the researcher. Within 1 month of receiving the NAACCR approval letter, the investigator was again contacted by NAACCR and was told more approvals by another NAACCR committee were required and the approval was withdrawn. Ultimately, after several more months, a letter was received by the investigator and was told by NAACCR the data would not be provided to the researcher; multiple reasons were given. During this process of awaiting the second NAACCR approval, the investigator was contacted by telephone by one of the NAACCR committee members; that particular committee member said that the dissertation research did not serve any scientific merit. NAACCR can be contacted via the URL http://www.naaccr.org/ . Any other future attempts to use the NAACCR data base were deemed unproductive therefore method one of contacting the states directly was utilized for the primary lung cancer case data collection. When the data sets were acquired from each state cancer registry, the data set was assessed for completeness of information, i.e. that the variables of interest were included.

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Also the categories for the variables of interest were evaluated for coding as the format would have to be similar as outlined by the standard NAACCR protocols. The comparison of results from state based registries for lung cancer cases would not be possible without this standardization. When each state cancer registry was contacted, each state representative was particularly interested in how the data are used for the specified or requested research project and that patient confidentiality would not be compromised. Additionally, it was requested that the database not shared with anyone other than the researchers identified on the data request form (the researchers must attest to this; in some instances the state data request form had to be notarized), and any confidential patient information inadvertently discovered must be kept confidential.

Statistical Procedures Prior to any statistical procedures, complete assessment of the study design methods was completed. The methods outlined in the selection criteria for cases identification and state cancer registry selections were based on epidemiological principles so ultimately valid assessments could be made after utilizing the most appropriate statistical procedures. In other words, data sets that contain inherent flaws due to bias would never result in invalid conclusions regardless of the statistical methods applied. For example, randomization during the selection of the regional state based cancer registry was part of the initial selection process. Any bias that may have been introduced by the selection of a state in theory was minimized by that randomization

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process. In the previous sections, the importance of the data selection or variables of interest (inclusion and exclusion criteria) was discussed. Collecting data from cancer registries that utilize a standardized format 258 was critical. Data not collected in such a manner could be subject to bias resulting in erroneous results. Each lung cancer case acquired from each state cancer registry was assessed for completeness of the data. In other words, all the variables of interest should be included in the case information and the categories for the variables of interest should be coded in a similar manner as outlined by the standard NAACCR protocols. The comparison of results from state based registries for lung cancer cases would not be possible without the standardization of the variables of interest. The selection of the most appropriate statistical model that best represented and accounted for the behavior of the data was critical in the evaluation of the three research questions. The statistical procedures applicable to each research question are discussed and reviewed in the following section; these procedures are utilized so that the research questions were answered appropriately. Each of the eight state based primary lung cancer case data was concatenated into one data set; this data set was used to answer the research questions. The lung cancer case information from each state is representative of the lung cancer cases that state (Idaho, Oregon, Florida, South Carolina, Indiana, Massachusetts, Nebraska, and Rhode Island) as each case within the state cancer registry has as likely a chance to be included in the state registry as another. The ability to identify which state the individual lung cancer case originated from in the merged data

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set will be done by means of a generated variable (code). This coding enabled the researcher to not only look at the aggregate population statistics but also by state and region. One of the first statistical procedures performed on the data sets was exploratory analysis. These descriptive procedures enabled the investigator to identify any differences, as well as the similarities of the patient population under study. This was accomplished by the PROC FREQ statistical procedure in SAS and univariate analysis by investigating the resultant means of the continuous variable age; results of the testing are given in Chapter Four. For example, it was useful to examine the difference between men and women, age of diagnosis, and state/region. Several studies recently published, suggest that the age at diagnosis of lung cancer is less for women versus men 40, 15 and the opportunity to compare the results of this data set analysis served to increase the validity of this research. Any differences in the lung cancer case population were determined for the number of men and women in each particular variable category such as morphology (histology and behavior) group, treatment group, or the age at tumor diagnosis categories. The results of the SAS PROC FREQ statistical procedure were examined for the other research variables of stage, grade, marital status at the time of diagnosis, race, vital status and state. The statistical procedures were used to count the frequencies of the data variables and calculated percentages. In summary, all categorical variables are displayed in Chapter Four tables and were also classified according to gender (male and female). The continuous variable of age at diagnosis was evaluated by the SAS PROC

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UNIVARIATE procedure and described in terms of the mean, medians and ranges comparing to males and females. Age was categorized into five age groups and those categorical classifications were used in the subsequent analysis to answer the three research questions.

Study Question One Null Hypothesis: Females receive the same treatment as men regardless of the histological type, stage and grade of lung cancer. The outcome variable is treatment, the exposure is gender (a main effect), and the variables classified as other main effect variables include stage, grade, and histological type; the demographic independent variables included age group at diagnosis, marital status at diagnosis and race. The statistical model used to examine the relationship between the dependent and independent variable, gender, will be the multinomial logistic regression model (MLRM). The multinomial logistic regression model facilitates the examination of the categorical outcome variable (Treatment Group) and the relationship between the independent variables. In this research, the outcome variable is multinomial or polychotomous and is coded on a nominal level. Each treatment type or treatment group (txgrp) or combination of treatments is categorized on a nominal scale meaning the levels (scale) do not represent a better or worse category. These nominal outcome levels and the independent variables were modeled or ―fitted‖ to a multinomial logistic regression model. There are eight treatment types (outcome variable levels) 1) radiation, 2) surgery, 3) chemotherapy, 4) radiation combined with surgery, 5) radiation combined with 139

chemotherapy, 6) surgery combined with chemotherapy, 7) radiation combined with chemotherapy and surgery, and 8) no treatment received that were analyzed to answer question one.

A representative equation for the full model is listed below and is defined as: Pr (yi=1) = G (0 + 1xi1 + 2xi2 +…+ kxik) G ( w) 

Where

ew 1  e w is the cumulative distribution function for a logistic variable and

upon transformation is referred to as the logit model. This model makes the assumption the chance (odds) of an outcome given a response level (in this case, a particular treatment modality received) are constant regardless of which level (treatment type) selected. For nominal outcome logistic models with k + 1 possible levels for the outcome variable, the logistic model can be extended to a multinomial model called a generalized or baselinecategory logit model, and is shown below: ln

/

(Pr(Y = i| x)) (Pr(Y = k+1| x))

= i + ’ixi, where i= 1,……,k

Where the 1, …, k are the k intercept parameters and the 1, …., k are the k vectors of the slope parameters; these models are designated as a special case of the discrete choice or conditional logit model. The coefficients resulting from multinomial logistic regression upon exponentiation are commonly referred to as an Odds Ratio. As gender was one of the main interests of this research, the effect that gender had

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on the selection of the treatment received classified gender as the primary ―main effect‖ variable for this particular research question (Question I). Each covariate, gender, stage, grade, morphology, age group, race, and marital status had different coding or levels within the particular variable and was discussed in the ―Variable of Interest‖ section of this chapter. The statistical results including the Odds Ratios and 95% Confidence Intervals generated by the MLRM included the main effects, interaction effects, and the overall gender effect on the outcome, i.e. treatment type received, are included in the overall assessment to answer Question One. In summary, the multinomial logistic regression model utilizing categorical variables was used as the statistical model to test Hypothesis I in order to answer question one. Additionally, a random effect model utilizing the SAS PROC GLIMMIX procedure with a link function to the generalized logit model was included to evaluate any effect state had the outcome as compared to another state. The identification of each state in the data set was important with respect to the study of any random effects introduced by a state on the relationship between the outcome (lung cancer treatment) and the independent variables. A random effect model was useful in the identification of one state that behaved differently (in the statistical sense) or having variability as compared to another state. As an example, a possible contributor to the ―random effect of state‖ would be that Florida is known as a retirement state. Florida’s population has an overrepresentation of individuals with a greater probability of cancer incidence. Statistics based on the treatment of Florida lung cancer female and male cases when compared to

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another state such as Nebraska (different population base) may lead to variability in the relationship of lung cancer treatment and the independent variables dependent upon state.

Study Question Two The statistical procedures to address the second study question (Is there a statistically significant difference in survival in women with lung cancer as compared to men with lung cancer regardless of the treatment modality received?) included the Kaplan-Meier and the Life Table methods for overall lung cancer survival analysis between men and women. The log-rank statistical test was utilized to test for survival differences between women and men by examining for any statistical significance. Survival was defined in this study as the time (in months) from the diagnosis of lung cancer to death or to the date of last contact when the individual was reported as alive – a cutoff date of 12-31-2004 was used to censor individuals that had a date of last contact greater than 12-31-2004.

Study Question Three The third research question, ―Do women with the same histological type, stage/grade of lung cancer, and the same treatment modality differ significantly in survival as compared to men with the same histological type, stage/grade of lung cancer, and the same treatment modality‖ utilized the Cox Proportional Hazards model. The Cox Proportional Hazards model estimated the relative risk or hazard ratio for death for women as compared to men. This model was used to address gender differences in overall survival while adjusting for the primary main effects, demographic main effects and interaction term 142

moderation. The effect of gender on survival was examined by determining the estimated relative risk (hazards ratio) of death for women as compared to men by adjusting by stage, histology, grade, treatment type, race, marital status, and age group as well as interaction terms with the adjusted Cox Proportional Hazards model. The proportionality assumption of the Cox’s Proportional Hazard model for each variable was tested by evaluating the graphs of the survival function and noting that the distance between the levels or strata of a variable did not change (increase or decrease) over time or cross. In each case, the proportionality assumption held with the exception of the variable, treatment groups. In this case there was crossover between two of the treatment groups suggestive of limitations in the analysis. Residual analysis was completed for the final model, there were some trends demonstrated in the Martingale Residuals over time but the majority of the residuals were varying about zero as expected - demonstrating no trends.

Preliminary Statistical Analysis Initially, a study was conducted by the investigator to determine if there were statistically significant differences between females versus males and the treatment received prior to the development of this dissertation. The data set was drawn from the Florida Cancer Data System 1 in which Commercial File 4505 had 2,393,853 cancer cases from the years 1981 through 2003. The lung cancer cases (n = 139,926) were categorized by the International Classification of Diseases – Oncology (ICD-O) and included the four major histological lung cancer types: adenocarcinoma, squamous cell

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carcinoma, large-cell carcinoma, and small-cell carcinoma. Other FCDS gender categories: 3 = Other (Hermaphrodite), 4) 4 = Transsexual and 5) 9 = Not Stated or Unknown, were excluded from the analysis as those particular gender categories did not contribute to the research question. The major treatment modalities (chemotherapy, surgery, hormone use, and radiation therapy) for FCDS lung cancer cases were included; other treatment modalities were excluded as this research focused on the major treatment modalities used to treat cancer. The major races/ethnic groups were selected based on the overall FCDS statistics; white and African-American were selected as the two racial/ethnic groups. Inclusion criteria for smoking status consisted of never smoking, past history of smoking, and presently smoking as referenced to the date of lung cancer diagnosis. The mean age for FCDS males (n = 88,248) was 68.96 years of age and for FCDS females (n = 51,678) 68.66 years of age was calculated with the SAS PROC UNIVARIATE program. Variable frequencies classified by gender were determined with the SAS PROC FREQ procedure. The majority of the FCDS women were married (14.2%), had a history of smoking (90.43%), and were white (n = 49,227 (95.26%)). Adjusted Odds Ratios were derived from the logistic regression model utilizing SAS Institute Inc., Cary, NC, USA Version 9.1 software. Based on a the statistics generated by a logistic regression model, there were statistically significant differences between gender and the treatment modality after adjusting for race, age and tobacco use. FCDS females had a decrease odds of receiving radiation therapy (OR = 0.939 (95%CI = 0.919, 0.961)) and surgery (OR = 0.940 (95%CI = 0.915, 0.966)) as compared to FCDS

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men. Additionally, there was decrease in the odds of having radiation therapy as a treatment modality for lung cancer for white FCDS females as compared to AfricanAmerican females (OR = 0.806 (95%CI = 0.771, 0.841)). FCDS African-American females had a decrease odds of receiving surgery as a treatment modality (OR = 0.605 (95%CI = 0.569, 0.643)) and chemotherapy (OR = 0.790 (95%CI = 0.751, 0.831)) for lung cancer as compared FCDS white women. FCDS females had a greater probability or risk of adenocarcinoma and small cell carcinoma as compared to FCDS males. Some of the limitations of investigator’s initial FCDS study were that the treatment groups were not stratified to examine a combination of receiving more than one treatment type nor were interaction terms considered in the relationship between treatment and gender.

Summary Initially, this research was based on a preliminary investigation of primary lung cancer cases for the Florida Cancer Data System 1 that studied if the treatment modality selected to treat a lung cancer case was based on gender. This research expanded the concept of lung cancer treatment differences based on gender to include the determination of survival differences in women as compared to men dependent upon the treatment modality received. The initial study objective was to investigate differences in major treatment modalities by gender, all four major histological lung cancer types combined by gender, and the major histological lung cancer types by gender. The relationship between treatment modalities and other variables (race, smoking status, vital

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status, and marital status) was evaluated. In particular, race was investigated to determine if any disparities between race and treatment type existed for FCDS women. This research expands on the preliminary findings and the patient population by including other NAACCR associated cancer registry lung cancer data in an attempt to determine if the particular treatment modality used to treat a woman with lung cancer affects her survival as compared to a man. As statistically significant differences in the association between gender and treatment have been demonstrated previously in the preliminary findings of this research, it was important to address these findings during the next phase of gender differences in lung cancer survival research. This study is a first step in the determination of survival in women with lung cancer and differences in treatment patterns as compared to men utilizing the data from state registries that are members of NAACCR. Results from this newly combined database will be in an attempt to quantify the extent of a gender specific treatment effect and the impact of this effect on survival. Another novel statistical approach in the study of gender differences in treatment selection and gender specific survival is the addition of interaction terms in the analysis. Also with the inclusion of interaction terms, the calculation of an overall gender effect of the treatment outcome and on survival could be possible. In the literature reviewed and cited throughout this dissertation, this approach has not been demonstrated. This approach adds another dimension in the study of gender differences for lung cancer treatments and survival as statistically significant results were demonstrated.

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CHAPTER IV: PRESENTATION AND ANALYSIS OF DATA Introduction This chapter presents the study findings. The study population consisted of lung cancer cases drawn from state based passive cancer registries in the United States. The lung cancer cases that were selected from each state cancer registry were intended to be representative of all the lung cancer cases for that particular state. For each state, the lung cancer case had equally as likely a chance of being included or excluded from the cancer registry. The study individuals were selected from state cancer registry lung cancer cases diagnosed during a five year time period, 1-1-2000 through 12-31-2004. The time or date of diagnosis served a dual purpose as that date was also used to specify the origin or start date for subsequent Survival Analysis. As previously stated in Chapter Three, the eight state cancer registries with the lung cancer cases were randomly selected from NAACCR US state cancer registries in four geographic regions. The reason for selecting cancer registries from four different geographic regions in the United States was reduce or eliminate any biases, e.g. selection, treatment, that may have been introduced by selecting cases from only one geographic region. The overall intent was to account for any differences in the population characteristics. Forty-six variables for each lung cancer case were requested from the eight NAACCR cancer registries. Each state reviewed the requested information and provided data that was consistent with their Internal Review Board (IRB) protocol, policies, and procedures. While some of the individual states did not provide information on all 46

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variables requested, the data provided by each state, did allow for a complete assessment so that the three research questions proposed in this study could be answered. Many of the study variables requested were intended to be utilized in a quality of data assessment. For example, evaluating the number of autopsies reported and comparing that frequency with Vital Status (alive versus dead) could be used to check the integrity of the data. As some data were either incomplete or unavailable to the researcher, a quality assessment or test could not be completed. Additionally, in the original request for specific variables, a number of states would not provide the variable information that they (the state registry) determined could possibly compromise the confidentially of a particular lung cancer case. Some of the state cancer registries made the determination of the variables or variables that were needed to answer the three research questions and provided only that information. As an example, the variables, ―date of diagnosis‖ and the ―date of last contact or date of death‖, were not provided in the South Carolina data set. Rather than providing the date of death, the South Carolina Cancer Registry computed ―survival time‖ for those lung cancer cases that were either died or alive. Survival time was calculated as the number of months from date of diagnosis to date of death or censure time (12/31/2004). Of the original 46 variables requested from the cancer registries, eleven variables were chosen to answer the three research questions. The list of variables in Table 31 include gender, stage of disease, grade of lung cancer, morphology (histology and behavior), treatment group, age at diagnosis, age group at the time of diagnosis, race,

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marital status at diagnosis, state of the cancer registry, vital status, and survival time (number of months from the date of diagnosis to date of death or censure time (12/31/2004)). Four variables of the original forty-six variables were selected as primary variables and are listed below in the Table 31. The primary independent variables are gender, morphology, stage, and grade and all four are included in the analysis to answer research questions one and three. When Hypothesis II for question two was tested, gender was used as the primary independent variable. Table 31 – Final Data Lung Cancer Set Variables Description of Variables Gender Morphology (Type and Behavior) Stage Grade Marital Status at Diagnosis Race Age at Diagnosis Group Vital Status Survival Time* Treatment Group State *Survival time in months: from date of diagnosis to date of death or censure time (12/31/2004)

From the originally requested 46 variables, several variables were intended to be used as quality indicators, be evaluated as possible confounders and to test for interaction effects. For the scope of this research, these are referred to as ―secondary‖ variables. Table 31 secondary variables include race, marital status at the time of diagnosis, and age group at the time of diagnosis. Treatment Group was used as a response variable for testing Hypothesis I and as an independent variable for testing Hypothesis III. Table 32 149

provides clarification and gives a description of the independent and outcome variables used to answer each of the research questions via hypothesis testing.

Table 32: Classification of Variables for Hypothesis Testing Independent Variables (Predictor)

Dependent Variables (Response)

Hypothesis I

Gender, Stage, Grade, Morphology, Race, Marital Status, Age Group, and State*

Treatment Group

Hypothesis II

Gender

Survival Time, Vital Status

Hypothesis III

Gender, Stage, Grade, Morphology, Race, Marital Status, Age Group, Treatment Group,

Survival Time, Vital Status

*State was used in a separate model when testing for any random effect, i.e. any effect that state could have on the relationship between the outcome and the independent variables.

In conclusion, eleven variables were utilized for the final analysis in order to answer the three research questions via hypothesis testing. The list of variables include gender, stage of disease, grade of lung cancer, morphology (histology and behavior), treatment group, age at diagnosis, race, marital status at diagnosis, state of the cancer registry, vital status, and survival time (number of months from date of diagnosis to date of death or censure time (12/31/2004)).

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Population Characteristics Demographics The demographic characteristics of the population under study from each state are given in Tables 33 through 36. There were a total of 44, 863 primary lung cancer cases included in the analysis after the study selection criteria for inclusion and exclusion were met and as outlined in Chapter Three. Briefly, the combined primary and secondary variable data set consisted of lung cancer cases that excluded individuals that were not diagnostically confirmed lung cancer, e.g. a diagnosis made by a cell/tissue sample or that were not analytic. For a lung cancer case to be considered analytic, one of three criterion must be met: (1) the diagnosis at the reporting facility and the entire first course of treatment was performed elsewhere or the decision not to treat was made at another facility, (2) the diagnosis at the reporting facility, and all or part of the first course of treatment was performed at the reporting facility, and (3) the diagnosis was made elsewhere, and all or part of the first course of treatment was performed at the reporting facility. Also, for each individual lung cancer, any missing or NOS (not otherwise specified) values for the primary and secondary variables were excluded. As shown in Table 33 below, Florida provided the major contribution of lung cancer cases at 24,602 (55.5% of all females, 54.9% of all males) with the overall data set minimum for lung cancer cases from Idaho (2.0% of all females, 2.0% of all males). As expected, for all states, there were a higher percentage of males with lung cancer as compared to females with lung cancer. Overall, the data set has 19,994 females (44.6%

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of the total lung cancer cases) and 24,869 males (55.4% of the total lung cancer cases) – shown in Table 36. Table 33: State Cancer Registries versus Gender Lung Cancer Distribution from the Eight State Cancer Registries Females Frequency Percent State Cancer Registry Florida Idaho Indiana Massachusetts Nebraska Oregon Rhode Island South Carolina Total

11089 400 2333 2823 702 735 459 1453 19994

55.5 2.0 11.7 14.1 3.5 3.7 2.3 7.3 100.0

Males Frequency Percentage

13513 496 3107 2992 1018 824 666 2253 24869

54.9 2.0 12.5 12.0 3.5 3.3 2.7 9.1 100.0

Total

24602 896 5440 5815 1720 1559 1125 3706 44863

The demographic characteristics for the lung cancer cases (gender, vital status, race, age group, and marital status at diagnosis) are listed in Table 24. The ages for the combined data set (primary lung cancer cases diagnosed between 1/1/2000 – 12/31/04) ranged from 40 - 89 years old. The mean age for the data set (N = 44,863) was 67.9 years, SD+10.2; for females (Nfemale = 19,994) the mean age was 67.9 years, SD + 10.4 and for males (Nmale = 24,869), the mean equaled 68 years, SD+10.0. For hypothesis testing, the continuous variable ―Age at Diagnosis‖ was classified into age groups (categorical variables); five ―Age Group-at-Diagnosis‖ strata or intervals were generated and are described in Table 24. The Age at Diagnosis (in years) Group 7 (> 70 - < 80) had greatest frequency of lung cancer cases with 16,404 (36.6 %), followed by Group 6 (> 60

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- < 70) with 13,536 (30.2 %), Group 5 (> 50 - < 60) 7,179 (16.0 %), and the minimum number in an age group was the > 40 - < 50 age interval, Group 4 with 2, 352 (5.2 %). Table 34: Lung Cancer Distribution Gender, Vital Status, Race, Age Group, and Marital Status at Diagnosis Frequency

Percent

Total

19994 24869 44863

44.6 55.4 100

Total

31869 12994 44863

71.0 29.0 100

41458 3042 363 44863

92.4 6.8 0.8 100

2352 7179 13536 16404 5392 44863

5.2 16.0 30.2 36.6 12.0 100

4427 26759 367 4920 8390 44863

9.9 59.6 0.8 11.0 18.7 100

Variable Gender Female Male Vital Status Dead Alive Race White Black Other Total Age Group at Diagnosis > 40 - < 50 yrs > 50 - < 60 yrs > 60 - < 70 yrs > 70 - < 80 yrs > 80 - < 90 yrs Total Marital Status at Diagnosis Single Married Separated Divorced Widowed Total

Originally, there were ten ―Age Group at Diagnosis‖ levels. Those age range

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groups not listed in Table 34 were > 0 - <10 years old, > 10 - <20 years old, > 20 - <30 years old, > 90 - <100 years old, and > 100 years old. The decision was made to limit the number of age groups based on the following: first, there were limited numbers of lung cancer cases that were younger than 40 and older than 90. The cumulative percent was less than 1% for the lung cancer data set for those lung cancer cases less than forty years of age and for those cases greater than 90 years old. An analysis and subsequent results would be subject error due to the limited sample size (decreased power or lack of ability to detect the ―effect‖ under study). Secondly, the population for the extremely young and extremely old, as referenced to lung cancer, is different and would not contribute to the relevance of the lung cancer cases selected for this research. In summary, the decision was made to exclude these age range groups. Seventy-one percent of the lung cancer cases (31,869) were classified under the variable ―Vital Status‖ as dead and 12,994 cases (29.0 %) were coded as alive. The study set, under Race, consisted mainly of ―White‖ lung cancer cases (41,458 (92.4 %)), with 3,042 (6.8 %) ―Black‖ and 363 (0.8%) cases were coded as ―Other‖. Table 34 also displays marital status at the time of lung cancer diagnosis. Approximately 60 percent of all the lung cancer cases (26,759, 59.6%) were classified as married at the time of diagnosis. The next classification with the greatest frequency was windowed (8,390, 18.7%) followed by divorced (4,920, 11.0%) and single (4,427, 9.9%) with the minimum number classified as separated of 367 (0.8%). The primary research variables (main effect) listed in Table 35 includes stage,

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grade, and morphology. Morphology coding, as previously stated in Chapter Three, includes coding for the histological type of lung cancer combined with the behavior code of the disease. All the primary lung cancer cases in this data set have a behavior code of 3, meaning all lung cancer cases in this data set were classified as malignant. Table 35: Lung Cancer Distribution Stage, Grade, and Morphology Frequency

Percent

Total

12028 4107 10359 18369 44863

26.8 9.2 23.1 40.9 100

Total

3153 12715 22417 6578 44863

7.0 28.3 50.0 14.7 100

Total

16139 13425 8473 6826 44863

36.0 29.9 18.9 15.2 100

Variable Stage I II III IV Grade I II III IV Morphology Adenocarcinoma Squamous Large Cell Small Cell

Stage IV lung cancer accounts for 40.9 % of the total four stage classification scheme with the minimum number of cases found with Stage II at 9.2 %. Adenocarcinoma was the major morphological type with 16,139 cases (36.0 %), squamous cell had the second highest frequency with 13, 425 (29.0 %), followed by large

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cell carcinoma with 8,473 (18.9 %) cases, and lastly, small cell carcinoma having 6,826 cases made up 15.2 % of the total four different stages of the lung cancer data base (N = 44,863). The grade of lung cancer (Table 35) consists of four classifications, most commonly found was Grade III (22,417, 50.0%); the Grade II lung cancers consisted of 12,715 (28.3%) cases, Grade IV (6,578, 14.7%), and Grade I had the minimum number of lung cancer cases of 3,153 (7.0%). One of the last tables of demographic data, Table 36-a, consists of the frequency and percent for state each cancer registry and the treatment groups. Of the eight states listed, Florida was the major contributor of the lung cancer cases as expected due to a greater number of residents – see Table 36-b for the 2000 – 2004 annual estimated population. Additional demographics for each state are provided in Appendix I in Tables 70 through Table 77. There are eight treatment classifications in Table 36-a which include a single treatment modality (Radiation Therapy (I), Chemotherapy (II), or Surgery (III)) treatment group, combinations of treatment modalities received (Radiation and Surgery (IV), Radiation and Chemotherapy (V), Surgery and Chemotherapy (VI), or Radiation combined with Surgery and Chemotherapy (VII) and the last classification consisted of lung cancer cases that received no treatment (Treatment Group VIII).

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Table 36-a: Lung Cancer Treatment Group and State Lung Cancer Distribution (Frequency and Percent) Frequency

Percent

Total

24602 896 5440 5815 1720 1559 1125 3706 44863

54.8 2.0 12.1 13.0 3.8 3.5 2.5 8.3 100

Treatment Group Radiation Chemotherapy Surgery Radiation + Surgery Radiation + Chemotherapy Surgery + Chemotherapy Radiation + Surgery + Chemotherapy No Treatment Total

4351 6472 12728 1063 7955 1249 1348 9697 44863

9.7 14.4 28.4 2.4 17.7 2.8 3.0 21.6 100

Variable State Florida Idaho Indiana Massachusetts Nebraska Oregon Rhode Island South Carolina

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Table 36-b: Total Population for the Eight States* Males State Florida Idaho Indiana Massachusetts Nebraska Oregon Rhode Island South Carolina Total

Females

Total

N

%

n

%

N

%

7,797,715 648,660 2,982,474 3,058,816 843,351 1,696,550 503,635 1,948,929 19,480,130

48.8 50.1 49.0 48.2 49.3 49.6 48.0 48.6 100

8,184,663 645,293 3,098,011 3,290,281 867,912 1,724,849 544,684 2,063,083 20,418,776

51.2 49.9 51.0 51.8 50.7 50.4 52.0 51.4 100

15,982,378 1,293,953 6,080,485 6,349,097 1,711,263 3,421,399 1,048,319 4,012,012 39,898,906

40.00 3.24 15.24 15.91 4.29 8.58 2.63 10.06 100

*Source: U.S. Census Bureau, Census 2000, and used as most current source of population statisitics for estimate purposes only

Although this research was focused primarily on specific treatment modalities, a proportion of lung cancer cases received no treatment (no radiation, chemotherapy, and/or surgery) were classified as Treatment Group VIII (Table 36-a). This classification allowed for the investigation of lung cancer cases that received no treatment by comparing the no treatment group to the other levels of the treatment groups. Treatment Group VIII had the second largest number of lung cancer cases as shown in Table 36-a with 9,697 (21.6%) subjects. One concern regarding the utilizing this treatment group (VIII) would be a possible bias being introduced from utilization of the ―no treatment received group‖ as the reference group during the statistical testing/analysis. For example, utilizing Treatment Group VIII as the reference group could introduce a differential classification bias. This bias would be resultant from using a set/group of lung cancer cases (VIII) that were different from all the other lung cancer cases

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(Treatment Group I - VII) as they (VIII) never received any treatment, i.e. radiation, chemotherapy, surgery, radiation + chemotherapy, radiation + surgery, chemotherapy + surgery, or radiation + chemotherapy + surgery. This could bias the null in any direction and any effect from the comparison of other treatment groups could be masked. Simply stated, the category of no treatment group used as a reference group cannot be lung cancer cases that are comprised from a different population. The population characteristics of the eight treatment groups were compared; there were no observable trends that suggested that Group VIII had any observable differences suggesting a dissimilar population mix. The following tables, Table 37 through Table 40, contain the assessment of the primary variable by the individual treatment groups. Each treatment group was evaluated for any dissimilarity or variability in gender, morphological type, stage, and grade of lung cancer. Table 37 displays the lung cancer distribution between the Treatment Groups versus Gender. In Table 37 for all treatment groups, the frequency of males with lung cancer is greater than females with lung cancer. When evaluating the eight treatment groups versus gender, the greatest number of males with lung cancer is found in Treatment Group III (Surgery only) with 6,718 cases. The minimum number of male lung cancer cases (612) was documented for Treatment Group IV (Radiation and Surgery). The maximum number of females (6010) was found in Treatment Group III (Surgery only) with the minimum number of female lung cancer cases (451) in Table 37 receiving a combination of Radiation and Surgery (Treatment Group IV).

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Table 37: Lung Cancer Distribution – Treatment Group vs. Gender Treatment Group Radiation I Chemotherapy II Surgery III Radiation + Surgery IV Radiation + Chemotherapy V Surgery + Chemotherapy VI Radiation + Surgery + Chemotherapy VII No Radiation, Surgery, and/or Chemotherapy VIII

Gender Frequency

Percent

Female Male

1772 2579

40.7 59.3

Female Male

2946 3526

45.5 54.5

Female Male

6010 6718

47.2 52.8

Female Male

451 612

42.4 57.6

Female Male

3346 4609

42.1 57.9

Female Male

585 664

46.8 53.2

Female Male

608 740

45.1 54.9

Female Male

4276 5421

44.1 55.9

Note: During statistical testing surgery was designated as the reference Treatment Group (VIII), the No Radiation , Surgery, and/or Chemotherapy Group (No Treatment Group) was designated as Treatment Group III

The next three tables compare the eight treatment groups with the primary variables of stage (Table 38), grade (Table 39) and morphology (Table 40). 160

Table 38: Lung Cancer Distribution – Treatment Group vs. Stage Treatment Group Radiation I

Chemotherapy II

Surgery III

Radiation + Surgery IV

Radiation + Chemotherapy V

Surgery + Chemotherapy VI

Radiation +Surgery+ Chemotherapy VII

No Radiation, Surgery, and/or Chemotherapy VIII

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Stage I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV

Frequency Percent 658 15.1 278 6.4 820 18.9 2595 59.6 337 5.2 277 4.3 1224 18.9 4634 71.6 8332 65.5 1847 14.5 1972 15.5 577 4.5 195 18.3 200 18.8 482 45.3 186 17.5 585 7.4 551 6.9 2677 33.7 4142 52.1 334 26.7 163 13.1 551 44.1 201 16.1 119 8.8 160 11.9 816 60.5 253 18.8 1468 15.1 631 6.5 1817 18.7 5781 59.6

The purpose of Table 38 was to compare the eight different treatment groups with the four stages of lung cancer. There were four treatment groups having the greatest percent of Stage IV lung cancer, Group I Radiation (59.6%), Group II Chemotherapy (71.6%), Group V Radiation and Chemotherapy (52.1%), and Group VIII (59.6%). One treatment group, the surgical treatment group, Group III, had the greatest percent (65.5%) for Stage I. Stage three lung cancers had the highest percentage in Group IV (Radiation and Surgery) at 45.3%, Group VI Surgery and Chemotherapy (44.1%) and Treatment Group VIII which combined all three treatment modalities: Radiation, Surgery, and Chemotherapy. Seven of the eight treatment groups (Table 39) had the highest percentage of lung cancers considered Grade III as compared to the other three grades: Treatment Group I (58.7%), Treatment Group II (47.5%), Group IV (52.5%), Group V (53.3%), Group VI (49.6%), Group VII (59.4%), and Group VIII (55.5%). There was only one treatment group (those receiving surgery only, Treatment Group III) in which the highest proportion (44.8%) of lung cancer cases were Grade II. In Table 40, Adenocarcinoma was the most frequent histological type of lung cancer for Treatment Groups III (46.2%), IV (45.6%), VI (46.3%), and VIII (33.7%). Squamous cell lung cancer was most common in Groups I (36.7%) and V (27.3%). Lastly, the most common histologic type of lung cancer for Group II Chemotherapy (38.5%) was Small Cell Lung Cancer (SCLC).

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Table 39: Lung Cancer Distribution - Treatment Group vs. Grade Treatment Group Radiation I

Chemotherapy II

Surgery III

Radiation + Surgery IV

Radiation + Chemotherapy V

Surgery + Chemotherapy VI

Grade I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV

Radiation +Surgery + Chemotherapy VII

I II III IV I II III IV

No Radiation, Surgery, and/or Chemotherapy VIII

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Frequency Percent 244 5.6 1171 26.9 2553 58.7 383 8.8 216 3.3 926 14.3 3072 47.5 2258 34.9 1627 12.8 5697 44.8 5194 40.8 210 1.7 63 5.9 418 39.3 558 52.5 24 2.3 280 3.5 1496 18.8 4239 53.3 1940 24.4 103 8.3 463 37.1 619 49.6 64 5.1 52 429 801 66 568 2115 5381 1633

3.9 31.8 59.4 4.9 5.9 21.8 55.5 16.8

Table 40: Lung Cancer Distribution – Treatment Group vs. Morphology Treatment Group Radiation I

Chemotherapy II

Surgery III

Radiation + Surgery IV

Radiation + Chemotherapy V

Surgery + Chemotherapy VI

Radiation + Surgery + Chemotherapy VII No Treatment VIII

Morphology Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell Adenocarcinoma Squamous Large Cell Small Cell

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Frequency Percent 1430 32.9 1596 36.7 980 22.5 345 7.9 1747 27.0 1087 16.8 1145 17.7 2493 38.5 5880 46.2 4610 36.2 2142 16.8 96 0.8 485 45.6 426 40.1 144 13.6 8 0.8 2106 26.5 2171 27.3 1597 20.1 2081 26.2 602 48.2 350 28.0 238 19.1 59 4.7 624 46.3 419 31.1 241 17.9 64 4.8 3265 33.7 2766 28.5 1986 20.5 1680 17.3

The tables and results of the analysis by treatment group versus the secondary variables are located in Appendix II - Tables 41 through 43. The tables display a comparison of the treatment groups versus age race (Table 41), marital status group at diagnosis (Table 42), and age group at the time of lung cancer diagnosis (Table 43). There were no obvious differences in treatment groups versus and distribution of race, Table 41 in Appendix II. The majority of lung cancer cases are White ranging from 91.3% of all lung cancer cases in Group V (Radiation and Chemotherapy) to 94% of all lung cancer cases in Group III (Surgery). The classification of ―Other‖ for race contained the least amount of lung cancer cases for each treatment group with each Treatment Group having a minimum of approximately one percent within each treatment classification (I – VIII). All treatment groups in Table 42 (Treatment Group vs. Marital Status at Diagnosis) have the greatest percentage of the lung cancer cases classified as married at the time of diagnosis ranging from 52.2 percent for Treatment Group VIII (no treatment) to maximum percentage of 69.9 percent for surgical and chemotherapy, Group VI. Lastly, each treatment group was evaluated for Age Group at Diagnosis. The treatment groups, I - VII did not display that Treatment Group VIII was any different or displayed any trends that would suggest that the patient population was not comparable to the other seven treatment groups. It was determined that the lung cancer cases in Group VIII were just as likely to receive a treatment modality or combination of treatment modalities when comparing the treatment patterns for Groups I through VII. The decision was made to include Group VIII for the analysis.

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Testing the Hypotheses Hypothesis I Women with the same histological type, stage and grade of lung cancer received the same treatment modality as compared to men with the same histological type, stage and grade of lung cancer. Introduction The first aim was to determine if men and women stratified by histologic type, stage, and grade of lung cancer received the same treatment type. The relationship between gender and the treatment modality received was evaluated including other independent variables and interaction terms. Interaction terms were included in the model to assess the role of moderating variables. A moderating variable can change the association (Odds Ratios) between the independent variable and the outcome variable at different levels of that moderator. It was important to establish if different treatments, e.g. radiation therapy, chemotherapy, surgery, were received based on gender; this has not been addressed specifically in the literature. Also after determining if the type of lung cancer treatment received was gender dependent, further analysis or study of that impact on gender specific survivorship could be addressed in Hypotheses II and III. As stated in Chapter Three, the statistical model selected to examine the relationship between the outcome variable (treatment group) and the independent variables was the multinomial, polychotomous or polytomous logistic regression model. The multinomial logistic regression or ―logit‖ models an outcome variable that has more than two

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outcomes; the research outcome variable, treatment group, was comprised of eight categories or eight different treatment selections. Table 44 lists the variables used during the testing of Hypothesis I; the variables were coded as categorical variables and were classified on the nominal scale. Also shown in Table 44 are the reference groups, for example Group VIII (Surgery) was utilized as the reference group during statistical testing with the generalized logit model.

Table 44: Outcome Variable and Independent Variables Variables for Testing Hypothesis I Multinomial Logistic Regression Model (MLR1)

I. II. III.

IV. V. VI. VII. VIII.

Outcome Variable* Treatment Group Radiation Therapy Chemotherapy No Treatment Assignment (No Radiation, Chemotherapy and/or Surgery) Radiation + Surgery Radiation + Chemotherapy Surgery + Chemotherapy Radiation + Chemotherapy + Surgery Surgery

Independent Variables** Gender Stage Grade Morphology Race Marital Status at Diagnosis Age Group at Diagnosis

* Outcome Variable Reference Group: Treatment Group = Surgery ** Independent Variable Reference: Gender = 2 (Male), Stage = IV, Grade = IV, Morphology = 4 (SCLC), Race = 3 (Other, Non-White), Marital Status = 5 (Widowed), and Age Group = 5 (> 80 - < 90 yrs)

The outcome variable profile given previously in Table 36 had a total frequency of 44,863 lung cancer cases; there was one outcome variable, treatment group, with eight treatment groups. The total frequency of each treatment group of lung cancer cases were

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also given in Table 36 with the minimum number of cases in treatment group IV (Radiation and Surgery) with 1,063 cases and a maximum of 12,728 lung cancer cases in treatment group VIII (Surgery).

Potential Confounders, Multicollinearity and Interactions This next section reviews three topics of interest: potential confounders, multicollinearity and interaction as each can impact the study by biasing the results due to the design and in the analysis phase. First, a potential source of error affecting the validity of study can be confounding variables. Confounding can cause a distortion in the measure of association due to an unequal distribution of a determinant of the outcome 73, 224

. Confounding is a problem of comparison, a problem that arises when important

extraneous factors are differentially distributed across groups being compared. A confounding variable is related to the outcome variable and the independent variable but not on the direct causal pathway between the outcome and independent variable of interest 73, 224. The following methods were utilized to control for confounding in the design phase of the study. In the study design phase, two methods selected to reduce any confounding were 1) randomization in the selection of the states and 2) restriction: some of the restrictions included selecting only primary lung cancer cases and cases from NAACCR cancer registries. Smoking is a major confounding variable for lung cancer but smoking was not addressed as a variable in this study due to several reasons. There was difficulty in

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studying the variable ―smoking‖ as cancer registry procedures and methods in the coding and collection of smoking history can be dissimilar and are listed below: I. Smoking was coded differently, e.g. the NAACCR standard code format was not followed in each state cancer registry. II. The data for smoking or smoking history were not collected in similar manner across all states, e.g. different start/inception dates to begin the collection of smoking information. III. No smoking history was collected or the information was not available from two state cancer registries under study, therefore a complete assessment could not be made. IV. Whether a person smokes or not is not a variable of interest in this research. In the opinion of the researcher, smoking or not smoking is not associated or rather will not determine if a lung cancer case receives a particular treatment modality, e.g. radiation, surgery, and or any combination of treatment modalities.

Next, a second possible source of error could be in the case of collinearity or highly collinear values between two independent variables that could affect the relationship of either or both variables on the research outcome, i. e. the lung cancer treatment received. This is commonly referred to as ―multicollinearity‖. When there is a high level of intercorrelation between the independent variables, the effects of the

169

independent variables may not be separated resulting in statistical inferences made about the data that could be unreliable. For the research data set, a similar method described by Hosmer and Lemeshow1 for categorical variables was utilized to test for multicollinearity. First, a logistic regression model was run or generated with all seven independent variable: gender, stage, grade, morphology, marital status, age group, and race. Then seven logistic regression models (variable subsets) were generated, i.e. each model dropped one of the independent variables that were originally included in the full model. The full model coefficients and standard errors were examined and then compared to the coefficients and standard errors for the seven other logistic regression models (Model I all variables, Model II stage excluded, Model III grade excluded, Model IV morphology excluded,

Model V gender excluded, Model VI marital status excluded, Model VII race excluded, Model VIII

age group excluded).

There were no appreciable differences between the standard errors and

the coefficients. An appreciable difference as noted by Hosmer and Lemeshow could be a change in the beta coefficients or standard errors by an order of magnitude. For example, in Table 45 the data extracted from the full model, Model I and Model II (morphology removed from the other independent variables) does not display such a change. From this assessment in combination all other ―subset‖ model comparisons, multicollinearity was deemed minimal; therefore the coefficients were assumed to be unbiased.

1

David W. Hosmer and Stanley Lemeshow ―Applied Logistic Regression‖ Second Edition Section 4.5 Pages 140 -141

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Table 45: Multicollinearity Assessment via Logistic Regression Comparison of Coefficients and Standard Error Extracted from the Logistic Regression Models Parameter

MODEL I* (Full Model)

Morphology Morphology Morphology Grade Grade Grade Stage Stage Stage Gender Race Race Marital Status Marital Status Marital Status Marital Status Age Group Age Group Age Group Age Group

1 2 3 1 2 3 1 2 3 1 1 2 1 2 3 4 4 5 6 7

Coefficient

Standard Error

-0.285 0.007 -0.016 -0.221 -0.198 0.189 -1.364 -0.381 0.321 -0.037 -0.097 0.103 0.061 -0.162 0.071 -0.021 -0.212 -0.162 -0.127 0.013

0.017 0.018 0.019 0.029 0.020 0.016 0.018 0.023 0.016 0.009 0.035 0.039 0.030 0.023 0.077 0.029 0.032 0.020 0.017 0.016

Grade 1 -0.324 0.027 Grade 2 -0.301 0.017 Grade 3 0.121 0.014 Stage 1 -1.363 0.018 Stage 2 -0.379 0.023 MODEL II* Stage 3 0.328 0.016 (Morphology Excluded) Gender 1 -0.047 0.009 Race 1 -0.091 0.035 Race 2 0.110 0.039 Marital Status 1 0.063 0.030 Marital Status 2 -0.170 0.023 Marital Status 3 0.074 0.077 Marital Status 4 -0.022 0.029 Age Group 4 -0.232 0.032 Age Group 5 -0.166 0.020 Age Group 6 -0.121 0.017 Age Group 7 0.025 0.016 *Model I included the independent variables of gender, stage, grade, morphology, marital status, race, and age group ** Model II included the independent variables of gender, stage, grade, morphology, marital status, race, and age group

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Another area of importance in the testing of the hypothesis was the evaluation of possible interaction or effect modification that might significantly affect the relationship between the outcome and the independent variables. An effect modifier is a variable that changes the relationship between the independent variable and the outcome variable. A variable that acts as an effect modifier is contained in the interaction term and the outcome/independent variable relationship changes at different levels or strata 73, 74, 226. The interaction terms were evaluated for their impact on the outcome and any effect on the overall fit of the model equation. In the statistical testing and analysis stage, interaction or effect modification was addressed by evaluating the stratified multivariate analyses. In summary, stratification based on the independent variables, such as stage, grade and morphological lung cancer type was employed in the statistical methods and interaction terms were included to evaluate the effects of any possible moderating variable on the relationship between the independent variable and outcome variable at different levels of that moderating variable.

Multinomial Logistic Regression Stepwise multinomial logistic regression (MLR) testing was used to select the variables (main effects and interaction terms) for the full model. The stepwise process consisted of a forward selection of covariates with a backward elimination of variables that did not meet a specified significance level. The stepwise procedure incorporated (specified in the model statement) the four primary variables (gender, stage, grade, and

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morphology) throughout the process selection and included variables with second degree interaction terms in the stepwise model selection process. The criterion for inclusion into a model was a significance level of 0.05 and an elimination of variable/interaction terms when the significance level was greater than 0.05. In the stepwise multinomial logistic procedure, Type III Analysis of Effects showed the change in the model fit when an independent variable was dropped while keeping the other variables in the model. In all, there were eleven different models generated. The resultant multinomial logistic model included seven main effects for the variables of gender, morphology, stage, grade, marital status, race, age group and five interaction terms of gender*morphology, gender*stage, stage*grade, gender*marital status and stage*age group. Table 46 gives the results for the statistical test that was generated for the final model, i.e. Type III Analysis of Effects. The Type III test statistic is associated with the estimated coefficients in the model and represents an effect due to a particular variable, e.g. gender. The statistic for the Type III test is the amount of variation in the response when a particular variable, e.g. gender, is added to the model that already contains all the other variables. Also, the Type III test statistic is not depended upon the order that the independent variables (to include interaction terms) are specified in the model. In the full model, gender was not significant (p-value >0.05) as a main effect but was significant in the interaction terms of gender and stage (gender*stage) and gender and marital status (gender*marital status). Other statistically significant interaction terms included stage and grade (stage*grade) and stage and age group (stage*age group). The

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interpretation of the exponentiated parameter estimates are presented as Odds Ratios with the associated 95% confidence intervals in the following paragraphs of this chapter. Also as noted in Table 46, there are large Wald Chi Square values for several main effect variables and interaction terms. Large Wald Chi-Square statistics are an indication of the variability of the data contained in the model. For example, this result could be attributed to the multinomial nature of the output variable, treatment group. The variability of the parameter estimates in the model could be increased due to the fact there are eight different levels of the outcome variable, treatment group.

Table 46: Type III Analysis of Effects Main Effect and Interaction Terms* Multinomial Logistic Regression Model (MLR1)

Effect Gender Morphology Gender*Morphology Grade Stage Gender*Stage Stage*Grade Marital Status Gender*Marital Status Age Group Stage*Age Group Race

DF 7 21 21 21 21 21 63 28 28 28 84 14

Wald Chi-Square 10.63 879.88 29.43 132.55 260.73 45.31 178.92 325.40 52.94 948.87 222.51 88.36

Pr > ChiSq 0.156 <.0001 0.104 <.0001 <.0001 0.002 <.0001 <.0001 0.003 <.0001 <.0001 <.0001

Note: Age Group and Marital Status at defined on/at Date of Diagnosis * indicates interaction term

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The Multinomial Logistic Regression (MLR) model (also known as the generalized or baseline-category logit model) with treatment group as the outcome variable is represented by the following equation (coefficients are not displayed):

=

Gender + Morphology + Gender*Morphology + Grade + Stage + Gender*Stage + Stage*Grade + Marital Status + Gender*Marital Status + Age Group + Stage*Age Group + Race

There were seven categories of the response variable (treatment group) for the multinomial logistic regression model. From the equation above, in the numerator Y represents the treatment type; when i = 1, the treatment group is radiation alone, if i = 2 the treatment group is chemotherapy alone, i = 3 the treatment group is no treatment, i = 4, the treatment group is radiation + chemotherapy, i =5 the treatment group is surgery + radiation, i = 6 the treatment group is surgery + chemotherapy, and when i = 7 the treatment group is radiation + chemotherapy + surgery. Also in the equation above, in the denominator Y is the reference treatment group for each of the treatment groups, 1 through 7. For the term Y (in the denominator), Y is the reference group and is the eighth treatment group (surgery). In the equation above, k + 1 is a numerical expression that specifies or is representative of the reference treatment group 8 (surgery); k = 7 therefore k +1 = Y =8. From Table 46, the following equation was derived from the full model (the interaction term of Gender*Morphology was not included because it was not statistically significant and therefore would not affect the outcome):

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Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI + others

The Wald statistic in Table 46 is a parameter/statistic that can be utilized to assess the ―goodness of fit‖ of the data to the model. In other words, the ―goodness of fit‖ can be interpreted as how well a mathematical equation estimates the behavior of the data. If a large variability of Wald Chi-Square (a statistic derived from the parameter estimate and standard error) existed, this could be interpreted that the wrong model was selected to examine the data. A brief review will be presented concerning the issue of how the assessment of the model fit was evaluated prior to reviewing the MLR model results. Model fit or the assessment of the predicted results versus the truth (the actual results from the data) for a statistical test can be performed after the analysis because the researcher wishes to ensure that he/she are using the correct method to test or assess their data and that the results are valid. Prior to inferences being made for the fitted model, an assessment of the model fit via diagnostics for the multinomial logistic regression model was made. Residual testing is a common statistical approach in the evaluation of the error in the model equation comparing the predicted or estimated results with the data. Because residual analysis was not available in the SAS software for a multinomial or multiple outcome variable (treatment) levels; Hosmer and Lemeshow2 suggest assessing

2

David W. Hosmer and Stanley Lemeshow ―Applied Logistic Regression‖ Second Edition Section 4.5

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the fit via logistic regression models for each outcome (seven logistic regression models in total). The results of the residual analysis were compared and examined for any trends that would demonstrate a lack of model fit. This type of assessment was made for the seven possible treatment outcomes of 1) radiation alone, 2) chemotherapy alone, 3) no treatment, 4) radiation + chemotherapy, 5) radiation + surgery, 6) surgery + chemotherapy, and 7) radiation + chemotherapy + surgery) with surgery as the reference group for each treatment group. Examples of the residual analysis results for three of the treatment outcome groups are given in Tables 52 through 54. No trends were demonstrated for the seven models meaning the use of the multinomial logistic regression model to the best of our knowledge was appropriate. In the next five sections, the statistical results obtained from the logistic regression models for the coefficient estimates, standard errors, the Odds Ratios and 95% confidence interval are evaluated. The first section reviews the ORs/95% CIs for the statistically significant main effect variables, morphology and race (Tables 47: a - b). Note these main effects are reported because they are not included in any statistically significant interaction terms. If morphology or race were in an interaction term that was statistically significant, that result due to the interaction would be reported. As the outcome could change due to the interaction; the outcome could be misinterpreted if the main effect variables were the only variables considered. In the second section, the MLRM ORs of the interaction terms (Tables 48 - 51) are evaluated. The third section compares and contrasts the results of the assessment of the model fit. The fourth section

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presents the results of a random effect component for state in the multinomial logistic regression model. The results given in the fifth section are the statistics and interpretation of the overall variable effect on the treatment type received.

Section 1: Multinomial Logistic Regression Main Effects When examining only main effect results from a statistical model, any type of effect modification between an independent variable and the outcome based on a moderating variable is not accounted for. When two variables interact in determining the chance of a particular outcome, it is inappropriate to just report the main effect as it will give misleading results. In this research the relationship between some of the main effect variables and the outcome variable changed when a moderating variable was present, as in the interaction term. The only time it is appropriate to report the main effect results is when that main effect variable gives statistically significant results and any interaction term containing that main effect variable is not statistically significant. Table 47-a consists of non-significant and significant OR’s and 95% confidence intervals generated in the full model, MLR1 for the main effect of morphology. The main effect of morphology is reported because this variable is significant and the interaction term of gender and morphology (gender*morphology) was not statistically significant (the results for the interaction of gender and morphology term are not reported in the final analysis). When evaluating morphology in Table 47-a, lung cancer cases with adenocarcinoma versus other lung cancer morphological types were at a statistically

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significant decreased risk for all treatment types (OR’s ranged from 0.03 – 0.23) with the exception of adenocarcinoma lung cancer cases receiving radiation therapy in combination with surgery (OR = 1.55, 95% CI 0.52, 2.65) as compared to receiving surgery after adjustment for gender, gender*morphology, grade, stage, gender*stage, stage*grade, marital status, gender*marital status, age group, stage*age group, and race. Comparing the Odds Ratios between adenocarcinoma and treatment type to lung cancer cases with squamous cell and treatment type, the same relationship was exhibited. There was an decreased risk that lung cancer cases with squamous cell carcinoma as compared to other lung cancer morphological types would receive any treatments (OR’s ranging from 0.04 to 0.28) with the exception of radiation in combination with surgery (OR = 1.92, 95% CI 0.64, 3.01) as compared to receiving surgery alone but this was not statistically significant. The last table for a main effect is Table 47-b, with race as the main effect with the outcome of treatment type. There was one statistically significant association between race and treatment type; no trends were exhibited for white and black lung cancer cases receiving a particular treatment versus receiving surgery. A possible limitation to this analysis of race was the reduced number of the reference group, other lung cancer cases (n = 363, 0.8%). There was an increased number of white (n = 41,458, 92.4%) and black lung (n = 3,042, 6.8%) cancer cases. This reduced number as the reference group could have introduced some bias into results (artificial inflation of the ORs). As the ORs only demonstrated one statistically significant result and as the variability of the confidence

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intervals was minimal, the decision was made to keep this group (other) as the referent group. Table 47-a: Main Effect for Morphology Extracted From the Full Model (MLR1) Odds Ratios and 95% Confidence Intervals by Treatment Group Treatment Type (Outcome)

Main Effect

Odds Ratio

95% LCI

95% UCI

Morphology Radiation Adenocarcinoma 0.14 0.10 0.52 Adenocarcinoma Chemotherapy 0.03 0.02 0.37 Adenocarcinoma No Treatment 0.07 0.05 0.41 Adenocarcinoma Radiation + Surgery 1.55 0.52 2.65 Adenocarcinoma Radiation + Chemotherapy 0.04 0.03 0.38 Adenocarcinoma Surgery + Chemotherapy 0.23 0.13 0.80 Radiation +Surgery + Chemotherapy Adenocarcinoma 0.21 0.12 0.75 Small Cell* Surgery* 1.00 Radiation Squamous 0.28 0.19 0.66 Squamous Chemotherapy 0.04 0.03 0.38 Squamous No Treatment 0.11 0.08 0.45 Squamous Radiation + Surgery 1.92 0.64 3.01 Squamous Radiation + Chemotherapy 0.08 0.06 0.42 Squamous Surgery + Chemotherapy 0.22 0.13 0.79 Squamous Radiation +Surgery + Chemotherapy 0.22 0.13 0.76 Surgery* Small Cell* 1.00 Radiation Large Cell 0.30 0.21 0.69 Chemotherapy Large Cell 0.06 0.04 0.41 No Treatment Large Cell 0.13 0.10 0.48 Large Cell Radiation + Surgery 1.64 0.55 2.75 Large Cell Radiation + Chemotherapy 0.10 0.07 0.44 Large Cell Surgery + Chemotherapy 0.34 0.19 0.92 Large Cell Radiation +Surgery + Chemotherapy 0.29 0.17 0.84 Surgery* Small Cell* 1.00 * Signifies Referent or Reference Group. Adjusted for gender, gender*morphology, grade, stage, gender*stage, stage*grade, marital status, gender*marital status, age group, stage*age group, and race.

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Table 47-b: Main Effect of Race Extracted From the Full Model (MLR1) Odds Ratios and 95% Confidence Intervals by Treatment Group Treatment Type (Outcome) Radiation Chemotherapy No Treatment Radiation + Surgery Radiation + Chemotherapy Surgery + Chemotherapy Radiation +Surgery + Chemotherapy Surgery*

Main Effect Race White White White White White White White Other *

Odds Ratio

95% LCI

95% UCI

0.99 0.67 0.86 0.88 0.83 1.31 0.85 1

0.62 0.45 0.59 0.43 0.56 0.62 0.46 -

1.46 1.08 1.23 1.58 1.23 2.05 1.48 -

Radiation Black 1.74 1.06 2.23 Black Chemotherapy 0.82 0.53 1.25 Black No Treatment 1.32 0.89 1.72 Black Radiation + Surgery 0.94 0.44 1.69 Black Radiation + Chemotherapy 1.22 0.80 1.63 Black Surgery + Chemotherapy 1.34 0.62 2.12 Black Radiation +Surgery + Chemotherapy 0.84 0.43 1.51 Surgery* Other * 1 * Signifies Referent or Reference Group. Adjusted for gender, morphology, gender*morphology, stage, grade, gender*stage, stage*grade, marital status, gender*marital status, age group, and stage*age group.

Section 2: Multinomial Logistic Regression Interaction Terms The next section lists the results that contain statistically significant interaction terms for the full model (Tables 48 – 51). In Table 48, the OR’s and the 95% confidence intervals are given for the interaction term of gender and stage. In this research, the overall gender effect is reported later in this chapter which utilizes the results of the main effect of gender and the interaction term of gender*stage. Also the results from Table 48 are compared later in the next section ―Multinomial versus Binomial Logistic Regression 181

Models‖. The Odds Ratios and 95% Confidence Intervals are given in Table 49 for the treatment groups and the interaction term of gender and marital status. The moderating variable of gender demonstrated the interaction effect varied according to the level of marital status in the interaction term (gender*marital status). Once again the statistics will not be discussed for Table 49 as this is covered in section five in the ―Gender Effect‖ portion of this chapter

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Table 48: Gender and Stage Interaction Terms Significant Terms Extracted From the Full Model (MLR1) Odds Ratios and 95% Confidence Intervals by Treatment Group Interaction Term Treatment Type (Outcome)

Odds Ratio

95% LCI

95% UCI

Gender Stage Female Radiation I 1.35 1.05 1.60 Female Radiation II 0.91 0.65 1.23 Female Radiation III 1.04 0.81 1.29 Male* IV* Surgery* 1 Female I Chemotherapy 1.53 1.14 1.82 Female II Chemotherapy 1.03 0.74 1.35 Female III Chemotherapy 1.29 1.02 1.52 Male* IV* Surgery* 1 No Treatment Female I 1.12 0.91 1.33 No Treatment Female II 1.16 0.89 1.41 No Treatment Female III 1.14 0.91 1.36 Male* IV* Surgery* 1 Radiation + Surgery Female I 0.95 0.61 1.39 Radiation + Surgery Female II 0.94 0.60 1.39 Radiation + Surgery Female III 1.07 0.72 1.47 Male* IV* Surgery* 1 Radiation + Chemotherapy Female I 1.17 0.91 1.42 Radiation + Chemotherapy Female II 0.99 0.76 1.27 Radiation + Chemotherapy Female III 1.26 1.02 1.48 Male* IV* Surgery* 1 Surgery + Chemotherapy Female I 0.99 0.67 1.38 Surgery + Chemotherapy Female II 0.84 0.53 1.30 Surgery + Chemotherapy Female III 1.25 0.86 1.63 Male* IV* Surgery* 1 Radiation +Surgery + Chemotherapy Female I 0.62 0.38 1.11 Radiation +Surgery + Chemotherapy Female II 0.68 0.43 1.14 Radiation +Surgery + Chemotherapy Female III 1.27 0.90 1.61 Male* IV* Surgery* 1 Note: * = The reference group LCI = Lower Confidence Interval, UCI = Upper Confidence Interval ** Adjusted for morphology, grade, stage*grade, marital status, gender*marital status, age group, stage*age group, and race

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Table 49: Gender and Marital Status Interaction Terms Significant Terms Extracted From the Full Model (MLR1) Odds Ratios and 95% Confidence Intervals by Treatment Group Interaction Term Odds Ratio 95% LCI 95% UCI Marital Gender Status Female Radiation Single 0.73 0.53 1.05 Female Radiation Married 0.88 0.71 1.10 Radiation Female Separated 1.67 0.69 2.56 Radiation Female Divorced 0.67 0.49 0.97 Male* Surgery* Widowed* 1 Chemotherapy Female Single 1.12 0.82 1.44 Chemotherapy Female Married 1.10 0.89 1.32 Chemotherapy Female Separated 2.47 1.03 3.35 Chemotherapy Female Divorced 1.04 0.77 1.34 Male* Surgery* Widowed* 1 No Treatment Female Single 0.76 0.58 1.02 No Treatment Female Married 0.94 0.78 1.12 No Treatment Female Separated 1.89 0.85 2.69 No Treatment Female Divorced 0.75 0.58 1.00 Male* Surgery* Widowed* 1 Radiation + Surgery Female Single 0.75 0.42 1.33 Radiation + Surgery Female Married 0.70 0.46 1.12 Radiation + Surgery Female Separated 0.65 0.10 2.47 Radiation + Surgery Female Divorced 0.65 0.37 1.20 Male* Surgery* Widowed* 1 Radiation + Chemotherapy Female Single 0.83 0.62 1.14 Radiation + Chemotherapy Female Married 0.78 0.63 0.99 Radiation + Chemotherapy Female Separated 1.59 0.71 2.40 Radiation + Chemotherapy Female Divorced 0.74 0.56 1.02 Male* Surgery* Widowed* 1 Surgery + Chemotherapy Female Single 1.50 0.83 2.08 Surgery + Chemotherapy Female Married 0.86 0.56 1.29 Surgery + Chemotherapy Female Separated 1.52 0.35 3.00 Surgery + Chemotherapy Female Divorced 0.69 0.40 1.25 Male* Surgery* Widowed* 1 Radiation +Surgery + Chemotherapy Female Single 1.38 0.78 1.95 Radiation +Surgery + Chemotherapy Female Married 1.07 0.72 1.47 Radiation +Surgery + Chemotherapy Female Separated 2.27 0.57 3.65 Radiation +Surgery + Chemotherapy Female Divorced 1.42 0.84 1.93 Male* Surgery* Widowed* 1 Note: * = The reference group LCI = Lower Confidence Interval, UCI = Upper Confidence Interval ** Adjusted for morphology, grade, stage, stage*grade, gender*stage, age group, stage*age group, and race Treatment Type (Outcome)

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Table 50 displays the results for the interaction term of stage and age group for treatment groups containing statistically significant ORs. The moderating variable of age group at the time of diagnosis demonstrated interaction between stage (independent variable) and the treatment group (outcome) based on the level of the moderator. Lung cancer cases with stage I lung cancer were 78% less likely to receiving radiation therapy treatments for Age Group V (OR = 0.22, 95% CI 0.13 – 0.75) and 63% less likely for Age Group VI (OR = 0.37, 95% CI 0.24 – 0.79) as compared to receiving surgery. This result was expected as radiation alone as a treatment for early stage lung cancer would not be the standard of care. For the other age groups with stage I lung cancer receiving radiation, the results were not statistically significant. Clinically, it would be predicted that the odds ratios for the youngest age group or Age Group 4 with early stage lung cancer would demonstrate a statistically significant decrease in the probability of receiving radiation alone but the results were not statistically significant (Table 50). For stage I lung cancer cases receiving radiation in combination with chemotherapy, there was a trend demonstrated that as age increased the ORs approached 1.00. Overall there was a decrease likelihood of being treated with chemotherapy combined with radiation. For early stage disease the youngest age group was 85% less likely to receive chemotherapy combined with radiation with the oldest age group (age group VII) being 32% less likely to receive radiation in combination with chemotherapy after controlling for gender, morphology, gender*morphology, grade, gender*stage, stage*grade, gender*marital status, marital status, and race. As shown in Table 50, 185

overall, the younger the early stage lung cancer case was, the less likely the lung cancer case had of being treated with radiation or radiation in combination with chemotherapy as compared to receiving surgery.

Table 50: Stage and Age Group at Diagnosis Interaction Terms Significant Terms Extracted From the Full Model (MLR1) Odds Ratios and 95% Confidence Intervals by Treatment Group Interaction Term Odds Ratio 95% LCI 95% UCI Stage Age Group Radiation I 4 0.08 0.03 1.14 Radiation I 5 0.22 0.13 0.75 Radiation I 6 0.37 0.24 0.79 Radiation I 7 0.69 0.47 1.08 Surgery* IV* 8* 1 Radiation III 4 0.31 0.14 1.08 Radiation III 5 0.48 0.29 0.97 Radiation III 6 0.71 0.46 1.13 Radiation III 7 0.87 0.58 1.28 IV* Surgery* 8* 1 Radiation + Chemotherapy I 4 0.15 0.07 0.92 Radiation + Chemotherapy I 5 0.27 0.16 0.79 Radiation + Chemotherapy I 6 0.42 0.26 0.88 Radiation + Chemotherapy I 7 0.68 0.44 1.13 IV* Surgery* 8* 1 Note: * = The reference group; LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 < 80 yrs), 8 = (> 80 - < 90 yrs).** Adjusted for gender, morphology, gender*morphology, grade, gender*stage, stage*grade, gender*marital status, marital status, and race Treatment Type (Outcome)

Table 51 contains the results for the interaction terms of stage and grade. There were no statistically significant ORs for Stage II at any level of Grade (the moderating variable) and was not presented in Table 51. Stage I and Stage III with the moderating variable of Grade that contained statistically significant results for a treatment group are listed. After adjustment for gender, morphology, gender*stage, stage*age group, marital 186

status, gender*marital status, age group, and race, grade II stage I lung cancer cases as compared to other lung cancer cases were 65% less likely to receive chemotherapy (OR = 0.35, 95% CI 0.20 – 0.93). This particular result was not unexpected as early stage disease, as the standard of care is not to receive chemotherapy as the only treatment for lung cancer; grade moderated the relationship between stage and the treatment received. Stage III grade I lung cancer cases as compared to other lung cancer cases were 7 times more likely (OR = 7.03, 95% CI 1.50 – 8.58) to receive radiation in combination with surgery after adjustment for gender, morphology, gender*stage, stage*age group, marital status, gender*marital status, age group, and race. The moderating effect of grade on the independent and the outcome relationship was highly significant for stage III grade I but this relationship was not significant for stage III with grade II or III. When considering just the ORs and not the confidence intervals this could be suggestive of a treatment difference base on grade as there was a trend of decreasing ORs with an increasing grade.

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Table 51: Stage and Grade at Diagnosis Interaction Terms Significant Terms Extracted From the Full Model (MLR1) Odds Ratios and 95% Confidence Intervals by Treatment Group

Treatment Type (Outcome) Chemotherapy Chemotherapy Chemotherapy Surgery* Radiation + Chemotherapy Radiation + Chemotherapy Radiation + Chemotherapy Surgery*

Interaction Term Stage Grade I I I II I III IV* IV* I I I IV*

I II III IV*

Odds Ratio

95% LCI

95% UCI

0.69 0.35 0.35 1.00

0.35 0.20 0.20 -

1.37 0.93 0.90 -

0.34 0.29 0.30 1.00

0.17 0.17 0.18 -

1.01 0.83 0.82 -

III I Radiation + Surgery 7.03 1.50 8.58 Radiation + Surgery III II 2.83 0.75 4.16 Radiation + Surgery III III 2.45 0.66 3.76 Surgery* IV* IV* 1.00 Note: * = The reference group; LCI = Lower Confidence Interval, UCI = Upper Confidence Interval ** Adjusted by gender, morphology, gender*morphology, gender*stage, gender*marital status, marital status, age group, stage*age group, and race.

Section 3: Multinomial Logistic Regression Assessment of Fit The comparison of the residual analysis results for the multinomial logistic regression models for the individual treatment are given next three tables (Figures 11 – 13). The results are given for the treatment groups of radiation therapy, chemotherapy, no treatment, radiation + surgery, radiation + chemotherapy, surgery + chemotherapy, and radiation + chemotherapy + surgery. The Pearson residual is the residual divided by the variance for a particular observation and is the individual contribution to the Pearson Chi Square statistic. The deviance residuals are a measure of the amount of deviance 188

contributed by the individual observation. In each distribution, the residuals are centered about zero, do not demonstrate a distinctive trend, and are similar for each treatment outcome. From the observed data of the residual patterns, the determination was made that the multiple logistic regression model was appropriate as a model for the research lung cancer data set.

Figure 11: Pearson and Deviance Residual Analysis Treatment Groups I, II, III with Treatment Group VIII (Surgery) as Reference

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Figure 12: Pearson and Deviance Residual Analysis Treatment Groups IV and V with Treatment Group VIII (Surgery) as Reference

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Figure 13: Pearson and Deviance Residual Analysis Treatment Groups VI and VII with Treatment Group VIII (Surgery) as Reference

Section 4: The Random Effect Component The statistical testing and analysis used in testing Hypothesis I also included a multinomial logistic regression model with a random effects component to investigate any random effect of state may have had on the model results. The method used in SAS was the Proc Glimmix procedure that utilizes statistical modeling approach to account for random effects. The ―random effect‖ procedure fit a random adjustment to the intercept of the model for the eight states in the cancer data set and estimates the variance of those

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adjustments separately for each level of the response variable, treatment group. In the Proc Glimmix procedure, the overall random effect of state was evaluated and in Table 52, the estimates of the variance are given. Because the variances do not demonstrate a wide range of variability, the random effect of eight states with respect to which treatment the lung cancer cases received meaning the heterogeneity (differences) was minimal.

Table 52: Random Effect of State Covariance Parameter Estimates: Intercept Method

Subject Random Effect

Group Treatment Group

State Radiation State Chemotherapy State No Treatment State Radiation + Chemotherapy State Radiation + Surgery State Surgery + Chemotherapy State Radiation + Chemotherapy + Surgery Note: Surgery is the reference treatment group

Estimate

Standard Error

0.340 0.247 0.094 0.091 0.331 0.101 0.024

0.175 0.126 0.050 0.060 0.168 0.061 0.016

When comparing the model without the random effect of state ―Type III Analysis of Effects‖ for the full multinomial logistic regression model (Table 46) with the multinomial logistic regression model ―Type III Analysis of Effects‖ (Table 53) generated with a random effect of state; there were no significant differences in the pvalues of the independent and interaction terms. Therefore, evaluating the Type III tests 192

for the model with and without the random effect, as the p-values did not change, the overall conclusions drawn during the Hypothesis I testing could be that the random effect is not influencing the conclusions but the random effect should be account for/assessed.

Table 53: Type 3 Analysis of Effects Main Effects and Interaction Terms* Multinomial Logistic Regression Model (MLR2) (Random Effect of State)

Effect Gender Morphology Gender*Morphology Grade Stage Gender*Stage Stage*Grade Marital Status Gender*Marital Status Age Group Stage*Age Group Race

DF 7 21 21 21 21 21 63 28 28 28 84 14

F Value 1.24 71.78 1.45 21.00 150.00 2.16 2.77 12.76 1.70 39.88 2.67 5.40

Pr > F 0.2739 <.0001 0.0851 <.0001 <.0001 0.0015 <.0001 <.0001 0.0115 <.0001 <.0001 <.0001

Note: Age Group and Marital Status at defined on/at Date of Diagnosis * indicates interaction term

Section 5: Overall Effect of Interaction on the Outcome By convention, the Odds Ratios (ORs) for the main effects and interaction terms are reported in the literature as a statistic used to evaluate the effect of the independent variables on the outcome of interest. In this section, the overall effect of interaction on the outcome of interest, treatment type received was also examined. In this research, the 193

ORs for the interaction effect on the treatment received was also evaluated in order to perform a more complete assessment of the relationship between the overall effect variables with the outcome. In determining the overall effect on the outcome, main effects and statistically significant interaction term variables that contained the variables of interest were included. In the equation below, the expression used to determine the overall interaction effect from the full model (variables listed in Table 46) is given as:

Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI + others

Over the next section and in Appendix IV, the method used to calculate the Odds Ratios and 95% Confidence Internals are given for the variable combinations of gender, stage, marital status, grade and age group. The summary of the overall effect variable combinations are given in Table 54. The statistically significant results for the overall interaction effects are given in Tables 55 through 57-b.

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Table 54: Overall Variable Effect on Lung Cancer (LC) Treatment Received Overall Interaction Effect Variable Gender Stage

Marital Status Grade Age Group

Interaction Term

Odds Ratios

Gender1*StageI Gender1*Marital Status1 StageI*Gender1 Stage1*GradeI StageI* Age Group1 Marital Status1*Gender1 Grade1*StageI Age Group1*StageI

OR = exp (1 + 6) OR = exp (1 + 7) OR = exp (2 + 6) OR = exp (2 + ) OR = exp (2 + ) OR = exp (3 + 7) OR = exp (4 + 8). OR = exp (5 + 9)

Note: Considering only Gender 1, Stage I, Grade I, Marital Status 1, Age Group 1 in this example; where Gender 1 = female, Age Group 1 = Age Group IV, and Marital Status 1 = Single

As previously discussed, a multinomial logistic regression model was utilized having eight treatment groups (Y). Surgery was the reference treatment group; therefore there were seven possible outcome categories or levels. Also, there were four categories for stage (Stage IV = reference) and five categories for marital status (Marital Status V (Widowed) = reference). An example is given next to show how the ―Gender Effect‖ Odds Ratios were calculated with the 95% Confidence Intervals for one of the treatment groups. In the equations below, the treatment group is radiation (with surgery as the reference) with gender = 1 for females and gender = 0 for males given for stageI and marital_statusI. The effect of gender on the probability of receiving radiation therapy as a treatment, given that the patient is at stageI, is determined as: Female: Logit (Y=Radiation|gender=1, stageI) =  + 1 + 2 stageI + 3 marital_statusI + 4 *stageI + 5* marital_statusI Male: Logit (Y=Radiation|gender=0, stageI) =  +2 stageI + 3 marital_statusI By subtracting the Logit for males from Logit for females, the following equation 195

is given as: Logit (Y=Radiation |gender=1, stageI) = 1 + 4 *stageI

+ 5* marital_statusI

Next, at the variable stageI which is coded as 1 for stage I and 0 for stage IV (reference), the results are: Logit (Y=Radiation |gender=1, stageI=1) = 1 + 4 Logit (Y=Radiation |gender=1, stageI=0) = 1

+ 5* marital_statusI + 5* marital_statusI

Marital_statusI appears in both logits whether stageI is 1 or 0. In other words, it does not matter whether the patient is single or married. This can also be stated as when the interaction between gender and stageI is examined, marital_status I is fixed or controlled for. For estimating the effect of gender (female as compared to male) on the probability of receiving radiation treatment, given that the patient is at stage I (stage=1) and after adjusting for marital_status I , the resultant equation for the Odds Ratio is given as: OR = exp (1 + 4). The Odds Ratios were calculated by exponentiating the beta coefficients for gender (female) plus the beta coefficient for the interaction term of gender (female)*stageI. The 95% lower (LCI) and upper (UCI) confidence intervals were calculated with the following equation: LCI, UCI = exp ((1 + 4) + (1.96* [(var (1) +var (4) + 2covar (1, 4)] 0.5)) For each of the six other possible outcomes (treatment groups) remaining, each outcome (chemotherapy, no treatment, radiation + surgery, radiation + chemotherapy, surgery + chemotherapy, radiation + chemotherapy + surgery) would have twenty possible results (ORs and 95% CIs) based on the level of stage for a total of 21 possible

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outcomes. As shown in Table 55 on the next page, females with stage I as compared with males with stage I are 1.71 times more likely to receive a combination of chemotherapy and radiation therapy versus receiving surgery alone (OR = 1.71, 95%CI 1.06 – 2.78) after adjustment. In other words for patients with stage 1 lung cancer, females are 1.71 times more likely to receive a combined chemotherapy and radiation therapy than males do. After adjustment, stage 3 females as compared to males with stage 3 were 1.85 times more likely to receive radiation therapy in combination with chemotherapy versus receiving surgery alone for the treatment of lung cancer (OR = 1.854, 95%CI 1.151 – 2.986). The results for females with stage II versus males with stage II lung cancer were not statistically significant. Also, none of the other six treatment types demonstrated statistically significant results.

Table 55: Interaction Effect of Gender on LC Treatment Received Gender and Gender*Stage Treatment Group 5 (Radiation Therapy in Combination with Chemotherapy) Odds Ratio

95% LCI

95% UCI

Females with Stage I (Treatment Group 5)

1.71

1.06

2.78

Females with Stage II (Treatment Group 5)

1.46

0.88

2.41

Females with Stage III (Treatment Group 5)

1.85 1.15 2.99 Reference for Gender = Males, Reference for Treatment Group = Surgery, Reference for Stage = Stage IV

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Next, the effect of gender on the probability of receiving radiation therapy as a treatment, given that the patient is at marital_statusI, is determined as: Female: Logit (Y=Radiation|gender=1, marital_statusI) =  + 1 + 2 stageI + 3 marital_statusI + 4 *stageI + 5* marital_statusI Male: Logit (Y=Radiation|gender=0, marital_statusI) =  +2 stageI + 3 marital_statusI By subtracting the Logit for males from Logit for females, the following equation is given as: Logit (Y=Radiation |gender=1, stageI) = 1 + 4 *stageI + 5* marital_statusI At the variable marital_statusI which is coded as 1 for marital_statusI and 0 for marital_statusI (V = reference), the results are: Logit (Y=Radiation |gender=1, marital_statusI =1) = 1 Logit (Y=Radiation |gender=1, marital_statusI =0) = 1

+ 4 *stageI + 4 *stageI

+ 5

Thus, from above stageI appears in both logits whether marital_statusI is 1 or 0. That is, it does not matter whether the patient is stage 1 or stage III. Another way of stating this fact is that when the interaction between gender and marital_statusI is looked at, stageI is fixed or controlled for. For estimating the effect of gender (female as compared to male) on the probability of receiving radiation treatment, given that the patient is at marital_statusI and after adjusting for stageI (stage=1), the resultant equation for the Odds Ratio is given as: OR = exp (1 + 5). For each of the six other possible outcomes (treatment groups) remaining, each outcome (chemotherapy, no treatment, radiation + surgery, radiation + chemotherapy, surgery + chemotherapy, radiation + chemotherapy + surgery) would have OR’s and 95%

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CIs based on the level of marital status for a total of 28 possible outcomes. The overall gender effect from gender and the interaction term of gender*marital status was also calculated for the treatment received. There were no statistically significant results.

Overall Effect of Stage on the Treatment Received In the next three tables (Tables 56-a, 56-b, 56-c), the statistically significant ORs and 95% CIs for the interaction effect of ―stage‖ on the outcome are given. In Table 56a, the interaction between stage and gender is examined with grade and age group at the time of diagnosis being controlled for. As noted in the table, all the ORs demonstrate a decrease probability to receive a particular treatment for females as compared to males. Females as compared to males with stage 1 and stage 2 lung cancer are less likely to receive one of the seven treatment types. The ORs range from 0.008 to 0.137 for stage 1 and 0.023 to 0.929 for stage II lung cancer. After adjustment, females versus males with stage I lung cancer are 86.3% less likely to receive a combination of surgery and chemotherapy (OR = 0.137, 95% CI 0.103 – 0.782). For stage 2 lung cancer, females as compared to males are 73.1% less likely to receive surgery in combination with chemotherapy (OR = 0.269, 95% CI 0.191 – 0.380). Stage 3 lung cancer females versus males after controlling for grade and age group, result in four treatment types (radiation, chemotherapy, no treatment and radiation in combination with chemotherapy) in which females can be up to 98% less likely to receive one of those four particular treatments. For example, females versus males with stage 3 lung cancer are 91.6% less likely to

199

receive radiation alone as their treatment for lung cancer (OR = 0.84, 95% CI 0.070 – 0.102) after adjustment. Also, contrary to the previous tables being presented with two significant figures, to see the variability between the statistics, the tables (Tables 56-a through Table 56-f-2) are presented to the third significant figure.

Table 56-a: Interaction Effect of Stage on LC Treatment Received Stage and Stage*Gender Treatment Type (Outcome)

Stage

Gender

OR

Radiation

I

Female

95% LCI

95% UCI

0.019

0.016

0.023

Chemotherapy

0.008

0.006

0.009

No Treatment

0.019

0.016

0.022

Radiation + Surgery

0.074

0.054

0.103

Radiation + Chemotherapy

0.013

0.011

0.015

Surgery + Chemotherapy

0.137

0.103

0.182

Radiation +Surgery + Chemotherapy

0.032

0.022

0.046

0.029

0.022

0.037

Chemotherapy

0.023

0.018

0.029

No Treatment

0.037

0.030

0.044

Radiation + Surgery

0.329

0.235

0.461

Radiation + Chemotherapy

0.047

0.038

0.058

Surgery + Chemotherapy

0.269

0.191

0.380

Radiation +Surgery + Chemotherapy

0.189

0.133

0.267

0.084

0.070

0.102

Chemotherapy

0.092

0.077

0.109

No Treatment

0.092

0.078

0.108

Radiation

Radiation

II

III

Female

Female

Radiation + Chemotherapy 0.019 0.016 0.023 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV, Reference for Gender = Males

200

The statistically significant ORs and 95%CIs for Table 56-b-1, 56-b-2, and 56-b-3 are given for the effect of stage on the probability of receiving a particular lung cancer given the lung cancer case is at a specific grade. After adjustment, for stage 1 grade 1 lung cancer cases as compared to stage 4 grade 4 lung cancer cases, stage 1 grade 1 lung cancer cases are 98.9% less likely to receive chemotherapy alone (OR = 0.011, 95% CI 0.007 – 0.017) or 88.1% less likely to receive radiation in combination with surgery (OR = 0.119, 95% CI 0.050 – 0.286). For grade 2 stages 1 though 3 (Table 56-b-2), once again there is a decreased probability to receive a particular treatment based on the ―stage effect‖ as compared to receiving surgery alone as the treatment for lung cancer. For stage 1 grade 2 and stage 2 grade 2, there are seven treatment types given as possible outcomes with four treatment types (radiation chemotherapy no treatment and radiation in combination with chemotherapy) for stage 3 grade 2. In Table 56-b-3, there are seven treatment type outcomes for stage 1 grade 3 and stage 2 grade 3 and five treatment outcomes for stage 3 grade 3. For all ―Stage Effects‖ in Tables 56-b-1through 56-b-3 there is a decrease probability to receive the particular treatment type listed, in other words for effect of stage does not increase the probability of receiving a treatment type. Also as the stage of the lung cancer patient increases there is a trend of increasing ORs.

201

Table 56-b-1: Overall Interaction Effect of Stage on LC Treatment Received Stage and Stage*Grade (Grade I) Treatment Type (Outcome)

Stage

Grade

OR

95% LCI

Radiation

I

I

0.025

0.017

0.037

Chemotherapy

0.011

0.007

0.017

No Treatment

0.032

0.024

0.045

Radiation + Surgery

0.119

0.050

0.286

Radiation + Chemotherapy

0.012

0.008

0.020

Surgery + Chemotherapy

0.126

0.067

0.238

Radiation +Surgery + Chemotherapy

0.070

0.028

0.172

0.047

0.027

0.083

Chemotherapy

0.028

0.015

0.053

No Treatment

0.050

0.032

0.076

Radiation + Chemotherapy

0.061

0.037

0.101

Surgery + Chemotherapy

0.312

0.146

0.668

Radiation +Surgery + Chemotherapy

0.150

0.044

0.503

0.221

0.138

0.352

Chemotherapy

0.129

0.077

0.215

No Treatment

0.184

0.123

0.274

Radiation

Radiation

II

III

I

I

95% UCI

Radiation + Chemotherapy 0.474 0.314 0.716 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV, Reference for Grade = Grade IV

202

Table 56-b-2: Interaction Effect of Stage on LC Treatment Received Stage and Stage* Grade (Grade II) Treatment Type (Outcome)

Stage

Grade

OR

Radiation

I

II

0.016

0.013

0.019

Chemotherapy

0.005

0.004

0.007

No Treatment

0.018

0.015

0.021

Radiation + Surgery

0.081

0.057

0.116

Radiation + Chemotherapy

0.010

0.008

0.013

Surgery + Chemotherapy

0.095

0.068

0.133

Radiation +Surgery + Chemotherapy

0.038

0.025

0.058

0.039

0.030

0.050

Chemotherapy

0.023

0.017

0.031

No Treatment

0.037

0.030

0.046

Radiation + Surgery

0.340

0.234

0.495

Radiation + Chemotherapy

0.051

0.040

0.065

Surgery + Chemotherapy

0.277

0.190

0.402

Radiation +Surgery + Chemotherapy

0.247

0.165

0.369

0.086

0.069

0.106

Chemotherapy

0.065

0.052

0.082

No Treatment

0.077

0.064

0.094

Radiation

Radiation

II

II

III

II

95% LCI

95% UCI

Radiation + Chemotherapy 0.183 0.151 0.222 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV Reference for Grade = Grade IV

203

Table 56-b-3: Interaction Effect of Stage on LC Treatment Received Stage and Stage* Grade (Grade III) Treatment Type (Outcome)

Stage

Grade

OR

95% LCI

95% UCI

Radiation

I

III

0.016

0.014

0.019

Chemotherapy

0.005

0.004

0.006

No Treatment

0.017

0.015

0.020

Radiation + Surgery

0.069

0.050

0.094

Radiation + Chemotherapy

0.010

0.009

0.012

Surgery + Chemotherapy

0.169

0.128

0.224

Radiation +Surgery + Chemotherapy

0.039

0.028

0.054

0.023

0.018

0.028

Chemotherapy

0.019

0.015

0.024

No Treatment

0.030

0.025

0.036

Radiation + Surgery

0.315

0.233

0.426

Radiation + Chemotherapy

0.041

0.034

0.049

Surgery + Chemotherapy

0.298

0.215

0.414

Radiation +Surgery + Chemotherapy

0.237

0.178

0.316

0.072

0.061

0.085

Chemotherapy

0.072

0.061

0.084

No Treatment

0.081

0.070

0.095

Radiation + Surgery

0.665

0.513

0.863

Radiation

Radiation

II

III

III

III

Radiation + Chemotherapy 0.162 0.139 0.188 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV, Reference for Grade = Grade IV

204

Next in Tables 56-c-1 through 56-c-3, the effect of stage on the probability of the treatment received given the patient is at age group I are given. For stage 1, there are seven possible outcomes with ORs ranging from 0.003 to 0.324 for age group 4, six outcomes for age group 5 (ORs range from 0.007 to 0.084), and 7 possible treatment outcomes for age group 6 and 7. Note that these results are based on comparing the reference group of surgery for the treatment type, age group 8 as the reference for the age group at the time of diagnosis and stage 4 as the reference group for stage. Table 56-c-2 gives the statistics for stage 2 and age groups 4 through 7. There are 5 treatment outcomes dependent upon the effect of stage (Stage 2 and age groups 4, 5, and 6) and four treatment groups for stage 2 in age group 7 with the ORs ranging from 0.038 to 0.080. Stage 2 age group 7 lung cancer cases have a 92.0% less likely probability to receive radiation therapy alone (OR = 0.080, 95% CI 0.35 = 0.182) versus receiving surgery after controlling for gender and grade. For stage 3, there are five possible treatment outcomes for age group 4 and 5 with age group 6 and 7 having four treatment types given with statistically significant ORs (Table 56-c-3). For age groups 6 and 7, the treatment types are radiation, chemotherapy, no treatment and radiation in combination with chemotherapy. The effect of stage on the treatment type received was to decrease the probability of receiving the treatment given in the tables. It is interesting to report that the effect of stage does not increase the probability of receiving a particular treatment type given stage 1, 2 or 3.

205

Table 56-c-1: Interaction Effect of Stage on LC Treatment Received Stage and Stage*Age Group (Stage I) Treatment Type (Outcome) Age Group Stage Odds Ratio 95% LCI 95% UCI Radiation 4 I 0.003 0.001 0.010 Chemotherapy 0.004 0.002 0.011 No Treatment 0.016 0.007 0.033 Radiation + Surgery 0.079 0.018 0.354 Radiation + Chemotherapy 0.010 0.004 0.022 Surgery + Chemotherapy 0.324 0.107 0.979 Radiation + Chemotherapy + Surgery 0.073 0.021 0.258 Radiation 5 0.008 0.004 0.017 Chemotherapy 0.007 0.004 0.013 No Treatment 0.017 0.009 0.030 Radiation + Surgery 0.075 0.021 0.271 Radiation + Chemotherapy 0.018 0.010 0.032 Radiation + Chemotherapy + Surgery 0.084 0.030 0.235 Radiation 6 0.014 0.007 0.026 Chemotherapy 0.013 0.007 0.022 No Treatment 0.024 0.014 0.040 Radiation + Surgery 0.129 0.038 0.443 Radiation + Chemotherapy 0.027 0.016 0.047 Surgery + Chemotherapy 0.308 0.132 0.716 Radiation + Chemotherapy + Surgery 0.104 0.039 0.279 Radiation 7 0.026 0.014 0.049 Chemotherapy 0.014 0.008 0.025 No Treatment 0.024 0.014 0.041 Radiation + Surgery 0.130 0.038 0.445 Radiation + Chemotherapy 0.045 0.027 0.076 Surgery + Chemotherapy 0.315 0.133 0.744 Radiation + Chemotherapy + Surgery 0.137 0.052 0.365 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Reference for Stage = Stage IV Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

206

Table 56-c-2: Interaction Effect of Stage on LC Treatment Received Stage and Stage*Age Group (Stage II) Treatment Type (Outcome) Age Group Stage Odds Ratio 95% LCI 95% UCI Radiation 4 II 0.009 0.003 0.034 Chemotherapy 0.012 0.004 0.040 No Treatment 0.024 0.010 0.060 Radiation + Chemotherapy 0.014 0.005 0.038 Radiation + Chemotherapy + Surgery 0.092 0.011 0.739 Radiation 5 0.025 0.010 0.062 Chemotherapy 0.020 0.008 0.050 No Treatment 0.026 0.012 0.055 Radiation + Chemotherapy 0.025 0.011 0.058 Radiation + Chemotherapy + Surgery 0.106 0.015 0.746 Radiation 6 0.043 0.018 0.098 Chemotherapy 0.037 0.015 0.087 No Treatment 0.037 0.018 0.076 Radiation + Chemotherapy 0.039 0.017 0.086 Radiation + Chemotherapy + Surgery 0.131 0.019 0.906 Radiation 7 0.080 0.035 0.182 Chemotherapy 0.042 0.018 0.099 No Treatment 0.038 0.019 0.077 Radiation + Chemotherapy 0.064 0.029 0.141 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Reference for Stage = Stage IV Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

207

Table 56-c-3: Interaction Effect of Stage on LC Treatment Received Stage and Stage*Age Group (Stage III) Treatment Type (Outcome) Age Group Stage Odds Ratio 95% LCI 95% UCI Radiation 4 III 0.013 0.004 0.043 Chemotherapy 0.034 0.012 0.098 No Treatment 0.061 0.027 0.139 Radiation + Chemotherapy 0.048 0.019 0.119 Radiation + Chemotherapy + Surgery 0.168 0.032 0.874 Radiation 5 0.034 0.016 0.073 Chemotherapy 0.055 0.025 0.119 No Treatment 0.066 0.034 0.126 Radiation + Chemotherapy 0.087 0.043 0.177 Radiation + Chemotherapy + Surgery 0.193 0.044 0.849 Radiation 6 0.058 0.029 0.115 Chemotherapy 0.101 0.050 0.205 No Treatment 0.094 0.051 0.172 Radiation + Chemotherapy 0.135 0.070 0.261 Radiation 7 0.109 0.056 0.212 Chemotherapy 0.116 0.058 0.232 No Treatment 0.096 0.053 0.175 Radiation + Chemotherapy 0.222 0.116 0.426 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Reference for Stage = Stage IV Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

Overall Effect of Marital Status on the Treatment Received For the effect of marital status on the probability of receiving a particular treatment given that the patient is female, Table 56-d demonstrates statistically significant ORs and 95% CIs for married, separated and divorced patients. Females versus males that were married were 48.2% less likely to receive radiation (OR = 0.518, 95%CI 0.388 – 0.690) or 45.3% less likely to receive no treatment (OR = 0.547, 95% CI 0.433 – 0.691). Married females versus married males were 2.144 more likely to receive surgery in combination with chemotherapy after adjustment. Also divorced females as compared 208

to males that were divorced were 1.738 time more likely to receive surgery in combination with chemotherapy. There was a probability of receiving no treatment for females versus males that were separated by as much as 72.8% (OR = 0.493, 95% CI 0.274 – 0.887).

Table 56-d: Interaction Effect of Marital Status on LC Treatment Received Marital Status and Marital Status *Gender Treatment Type (Outcome)

Gender

Marital Status

Odds Ratio

95% LCI

95% UCI

Radiation

Female

Married

0.518

0.388

0.690

No Treatment

0.547

0.433

0.691

Surgery + Chemotherapy

2.144

1.335

3.444

0.493

0.274

0.887

No Treatment

Female

Separated

Surgery + Chemotherapy Female Divorced 1.738 1.012 2.985 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Reference for Marital Status = Widowed

Overall Effect of Grade on the Treatment Received In examining the effect of grade on the probability of receiving a particular treatment type, for stage 1, 2, and 3, there is a decreased probability in all instances listed in Table 56-e-1. For stages 1, 2 and 3, there are seven possible treatment outcomes for grade 1. As the severity of the grade increase, there are less treatment types given as outcomes that are statistically significant. For grade 2 stage 1, there are two statistically significant ORs/95% CIs for the treatment types of chemotherapy and radiation in combination with chemotherapy. In Table 56-e-3, the effect of grade 3 on the probability 209

of the treatment type received are given. For grade 3 stage 1 there 1 treatment type listed that is statistically significant (radiation in combination with chemotherapy) and three treatment types for stage 2 grade 3 and stage 3 grade 3. The ORs for grade 3 range from a minimum 0.385 to a maximum of 0.570.

Table 56-e-1: Overall Interaction Effect of Grade on LC Treatment Received Grade and Stage*Grade (Grade I) Treatment Type (Outcome) Stage Grade Odds Ratio 95% LCI 95% UCI Radiation I I 0.445 0.267 0.741 Chemotherapy 0.254 0.157 0.411 No Treatment 0.526 0.372 0.744 Radiation + Surgery 0.320 0.113 0.901 Radiation + Chemotherapy 0.127 0.080 0.202 Surgery + Chemotherapy 0.331 0.179 0.613 Radiation + Chemotherapy + Surgery 0.300 0.118 0.761 Radiation II 0.297 0.168 0.528 Chemotherapy 0.129 0.076 0.218 No Treatment 0.299 0.191 0.469 Radiation + Surgery 0.224 0.065 0.765 Radiation + Chemotherapy 0.107 0.066 0.173 Surgery + Chemotherapy 0.247 0.108 0.565 Radiation + Chemotherapy + Surgery 0.158 0.053 0.472 Radiation III 0.327 0.187 0.572 Chemotherapy 0.130 0.079 0.211 No Treatment 0.295 0.191 0.457 Radiation + Surgery 0.190 0.056 0.644 Radiation + Chemotherapy 0.112 0.071 0.178 Surgery + Chemotherapy 0.434 0.193 0.973 Radiation + Chemotherapy + Surgery 0.159 0.055 0.462 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV, Reference for Grade = Grade IV

210

Table 56-e-2: Overall Interaction Effect of Grade on LC Treatment Received Grade and Stage*Grade (Grade II) Treatment Type (Outcome) Stage Grade Odds Ratio 95% LCI 95% UCI Chemotherapy I II 0.380 0.211 0.682 Radiation + Chemotherapy 0.210 0.118 0.373 Radiation II 0.496 0.313 0.786 Chemotherapy 0.193 0.128 0.290 No Treatment 0.431 0.312 0.596 Radiation + Chemotherapy 0.177 0.125 0.251 Surgery + Chemotherapy 0.343 0.197 0.600 Radiation + Chemotherapy + Surgery 0.322 0.145 0.716 Radiation III 0.545 0.331 0.897 Chemotherapy 0.194 0.127 0.296 No Treatment 0.426 0.293 0.618 Radiation + Chemotherapy 0.186 0.126 0.273 Radiation + Chemotherapy + Surgery 0.326 0.139 0.765 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV, Reference for Grade = Grade IV

Table 56-e-3: Overall Interaction Effect of Grade on LC Treatment Received Grade and Stage*Grade (Grade III)

Treatment Type (Outcome) Stage Grade Odds Ratio 95% LCI 95% UCI Radiation + Chemotherapy I III 0.434 0.246 0.765 Chemotherapy II 0.401 0.255 0.632 Radiation + Chemotherapy 0.366 0.245 0.547 Surgery + Chemotherapy 0.325 0.168 0.627 Chemotherapy III 0.403 0.278 0.584 Radiation + Chemotherapy 0.385 0.277 0.533 Surgery + Chemotherapy 0.570 0.331 0.982 Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Stage = Stage IV, Reference for Grade = Grade IV

211

Overall Effect of Age Group on the Treatment Received In the next two tables, Tables 57-a and 57-b, the effect of age group of the probability of receiving a particular treatment type given the patient is at stageI are given. These tables display statistics that show not only a decreased probability of receiving a specific treatment but also an increased probability to receive a particular treatment based on the overall interaction effect of age group. For age group 4 and 5, there is an increased probability to receive surgery + chemotherapy as well as radiation + chemotherapy + surgery for all stages (stages 1, 2, and 3). For stage 1 age group 4 patients, there is a 5.716 times increased in the risk to receive surgery in combination with chemotherapy and a 7.975 increase risk to receive radiation in combination with surgery plus chemotherapy. Of all age groups, the maximum risk (OR = 11.377, 95% CI 3.387 – 38.214) to receive a particular treatment (radiation + chemotherapy + surgery) is for age group 4 stage 3 lung cancer patients. There is a decrease probability to receive radiation or no treatment for age groups 4, 5, 6, and 7 for all three stages of lung cancer (Tables 57-a and 57-b) after adjustment. The ORs range from 0.044 for age group 6 stage 1 to 0.382 for age group 4 stage 3. Age group seven is the only age group that lists ORs that a show a decreased risk to receive a given treatment type. This could be indicative of that as patients’ age - patients are not treated as aggressively.

212

Table 57-a: Interaction Effect of Age Group on LC Treatment Received Age Group and Stage*Age Group (Age Group 4 and Age Group 5) Treatment Type (Outcome)

Stage

Age Group

OR

Radiation

I

4

0.085

0.035

0.210

No Treatment

0.278

0.191

0.406

Surgery + Chemotherapy

5.716

2.930

11.150

Radiation + Chemotherapy + Surgery

7.975

2.419

26.285

0.225

0.118

0.429

No Treatment

0.299

0.171

0.525

Radiation + Surgery

3.260

1.136

9.356

Surgery + Chemotherapy

7.375

2.851

19.082

Radiation + Chemotherapy + Surgery

9.164

2.650

31.688

0.382

0.219

0.667

No Treatment

0.426

0.255

0.712

Radiation + Surgery

5.636

2.084

15.245

Radiation + Chemotherapy

2.313

1.313

4.074

Surgery + Chemotherapy

5.429

2.212

13.328

Radiation + Chemotherapy + Surgery

11.377

3.387

38.214

Radiation

Radiation

Radiation

II

4

III

4

0.057

0.020

0.161

0.340

0.138

0.837

No Treatment

0.211

0.112

0.397

Radiation + Chemotherapy

0.409

0.195

0.859

Surgery + Chemotherapy

3.110

1.140

8.488

Radiation + Chemotherapy + Surgery

4.624

1.194

17.914

0.150

0.103

0.217

Chemotherapy

0.542

0.339

0.867

No Treatment

0.226

0.179

0.286

Surgery + Chemotherapy

4.013

2.243

7.181

Radiation + Chemotherapy + Surgery

5.314

1.841

15.343

0.254

0.173

0.371

No Treatment

0.322

0.233

0.445

Radiation + Surgery

4.125

1.822

9.338

Surgery + Chemotherapy

2.954

1.427

6.116

Radiation + Chemotherapy + Surgery

6.597

2.162

20.129

Radiation

5

95% UCI

Chemotherapy

Radiation

I

95% LCI

II

5

III

5

Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Reference for Stage = Stage IV Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

213

Table 57-b: Interaction Effect of Age Group on LC Treatment Received Age Group and Stage*Age Group (Age Group 6 and Age Group 7) Treatment Type (Outcome)

Stage

Age Group

OR

Radiation

I

6

0.044

0.016

0.122

Chemotherapy

0.252

0.104

0.611

No Treatment

0.187

0.101

0.345

Radiation + Chemotherapy

0.250

0.121

0.516

Surgery + Chemotherapy

2.915

1.111

7.650

0.115

0.072

0.184

Chemotherapy

0.402

0.233

0.692

No Treatment

0.201

0.141

0.288

Radiation + Chemotherapy

0.457

0.292

0.714

Surgery + Chemotherapy

3.762

1.793

7.892

0.196

0.152

0.251

No Treatment

0.286

0.241

0.340

Radiation + Chemotherapy

0.705

0.520

0.954

Surgery + Chemotherapy

2.769

1.579

4.856

Radiation + Chemotherapy + Surgery

3.002

1.063

8.476

0.045

0.016

0.125

Chemotherapy

0.206

0.085

0.500

No Treatment

0.220

0.120

0.403

Radiation + Chemotherapy

0.161

0.078

0.331

0.119

0.075

0.188

Chemotherapy

0.329

0.192

0.564

No Treatment

0.236

0.166

0.335

Radiation + Chemotherapy

0.294

0.189

0.457

0.201

0.145

0.280

Chemotherapy

0.608

0.395

0.938

No Treatment

0.336

0.258

0.438

Radiation

Radiation

Radiation

Radiation

Radiation

II

6

III

6

I

7

II

7

III

7

95% LCI

Note: OR = Odds Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Reference for Stage = Stage IV Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

214

95% UCI

Hypothesis I Conclusion

In conclusion, in testing Hypothesis I, the null hypothesis of no differences in treatment outcomes between men and women, was rejected as there were statistically significant results when the overall gender effect was examined. A multinomial logistic regression model was used to test Hypothesis I for differences between men and women with the same histological type, stage, and grade of lung cancer and the treatment they received. When considering the random effect that state may have made on the conclusions, the reported estimates of the variances demonstrated that there was minimal heterogeneity due to the states with regard to which treatment modality the lung cancer cases received. Also when comparing the p-values for the Type III tests for the model with and without the random effect, the overall conclusions drawn during Hypothesis I testing did not change. Although it could be said the random effect was not influencing the overall conclusions, the random effect should be assessed for the possible impact on the resultant analyses. Lastly, when the overall interaction effect of gender, stage, grade, marital status and age groups on the treatment received was examined, a complete assessment of the outcome was given. For example, utilizing gender and the interaction terms of gender*stage and gender*marital status, statistically significant ORs and 95% CIs were demonstrated for an increased or risk to receive a particular treatment modality based on gender and a specific stage classification. 215

Hypothesis II There is a statistically significant difference in survival between women with lung cancer as compared to the survival of men with lung cancer. Introduction and Survival Analysis During hypothesis testing, the purpose for Hypothesis 2 was to examine if there was or was not an association between gender and survival without adjustment for the other research covariates in the lung cancer data set. To test Hypothesis II and assess the relationship between gender groups and survival, a non-parametric survival method, the Life Table (Actuarial) method was utilized. The life table method is appropriate for large data sets with grouped data and the observation times are subdivided into intervals of fixed length. Table 58 lists the results of the survival distribution function generated by the Life Table method. Although three test statistics (the log-rank, the Wilcoxon, and -2Log (LR)) were generated for the survival distribution function, the log-rank test statistic was selected as the standard reporting statistic. This particular statistic gives equal weight to every lung cancer case death time. The other two statistics adjust for differences in the survival distribution function depending upon the time of the death in the time, i.e. the event (death) will be weighted. The log-rank test statistic was significant with a p-value less than 0.0001 which could be interpreted as a difference existed in survival between men and women.

216

Table 58: Lung Cancer Survival Survival Distribution Function Testing the Equality over Gender

Test Log-Rank

Life Table Method ChiChi-Square Square DF* P Value 213.70 1 <.0001 *DF = Degrees of Freedom

The graph of the survival function or cumulative survival, S(t) for Life Table method is shown below in Figure 14. As displayed in the graph, with no adjustment, females had an increased probability of survival and survived longer than males. The shape of the curves for the survival probability for males and females in Figure 14 were not the same, i.e. the curves did not overlay and did not cross. ―If‖ the survival curves for females and males did overlay, this would suggest that there was no difference with a resultant ―failure to reject‖ the null hypothesis. In Figure 14, any crossing of the genderspecific survival curves could indicate changes in the survival patterns between males and females or possible interaction. Also shown in Figure 14 after 10 months, the difference between the curves were approximately parallel over the 5 year time period; this suggested that the proportionality assumption was not violated meaning the hazard or risk of death did not change over time between the male and female lung cancer cases.

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Figure 14: Life Table Method Survival Distribution Function

Table 59 displays the summary of the number of lung cancer case that lived (no event = censored) and the number of lung cancer cases that died (event occurred = uncensored). During survival statistical testing it was important to evaluate the number of cases that died and the number of cases that lived in the data set because if the number of lung cancer cases that lived were disproportional between the two groups (females and males) the resultant statistics could be biased and subsequent interpretations for Hypothesis II could be limited. Table 59 gives the total number of females and male lung cancer cases in the lung cancer data set; overall 33.31% of the female lung cancer

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cases did not die and 25.47% of the male lung cancer cases did not die over the study 5 year time period. In Table 59, although fewer males lived as compared to females (6333males vs. 6661females) when examining Table 59-a, the differences in the number of females and males that lived in time intervals (1 month intervals) was not disproportionate. Overall, the survival time consisted of 60 time intervals of one month, examples (Table 59-a) for the early time intervals and later time intervals are given for number of cases that died (d), the number of cases that survived (c) and the effective number of lung cancer cases at risk (n) in each time interval, I (one month). Therefore, the difference between the number of female and male lung cancer cases that lived (c) would not impact or limit the interpretation of the survival results from Hypothesis II testing.

Table 59: Survival Data for Lung Cancer Cases Lung Cancer Distribution Summary

Gender Female Male Total

Total Total Died 19994 13333 24869 18536 44863 31869

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Total Lived 6661 6333 12994

Percent Lived 33.31 25.47 28.96

Table 59-a: Extracted Life Table Survival Parameter Results Life Table Model for the Lung Cancer Data Set Time Interval (months) Lower, Upper

Females

Males

0 1 2 3 4 56 57 58 59

1 2 3 4 5 57 58 59 60

Number Failed (Died) d 2665 1310 1082 967 821 7 4 4 1265

0 1 2 3 4 56 57 58 59

1 2 3 4 5 57 58 59 60

3672 1877 1617 1445 1215 7 5 9 1394

Number Censored (Lived) c 1436 656 452 379 363 12 11 10 619

Effective Sample Size n 19276.0 15565.0 13701.0 12203.5 10865.5 1926.0 1907.5 1893.0 1574.5

1278 641 459 375 360 3 18 11 526

24230.0 19598.5 17171.5 15137.5 13325.0 1971.5 1954.0 1934.5 1657.0

Note: Originally there were 60 time intervals of 1 month

In Table 60, the results for the quartile estimates and the 95% confidence intervals for the survival probabilities with the mean survival times are given for males and females lung cancer cases. The confidence intervals are reported because each estimate of the survival probability contains random variation resulting in an inherent imprecision. When evaluating the ―Quartile Percents‖, the 50th percentile is of the greatest of interest as it represents the median survival time. The median survival time is defined as the survival time for a cumulative survival function of 0.5. The median survival time for the 220

data set cannot be interpreted as that ―point in time‖ where 50% of the lung cancer cases survived; this would only be true if there were no censored observations prior to that ―point in time‖, which was not the case. Females had a median survival of 7.69 months with a 95% confidence interval of 7.46 to 7.95 months. The median survival time for males was 6.30 months with a 95% confidence interval of 6.17 to 6.44 months; this was 1.39 months less than the female median survival time. Also shown in Table 60, the mean survival time for females is 19.83 months whereas the mean (or average) survival time for males was 16.37 months; females for this data set ―on average‖ lived approximately 3.01 months longer than the males.

Table 60: Gender Survival Estimates (in months) Summary Statistics of the Lung Cancer Distribution Quartile Point Percent Estimate Female 75 34.29 50 7.69 25 2.63 Male

75 50 25

19.70 6.31 2.23

95% LCI

95% UCI

Mean Standard Error

31.56 7.46 2.53

37.41 7.95 2.73

19.83

0.19

18.85 6.17 2.14

20.46 6.44 2.30

16.37

0.15

*LCI = Lower Confidence Interval, UCI = Upper Confidence Interval

Another test statistic generated with the Life Table Method is the cumulative hazard function, CHF. The cumulative hazard function corresponds to the total number of deaths over an interval of time. In Figure 15, the graph of the cumulative hazard function is representative for the overall study time of 60 months. The x-axis for the 221

overall time is annotated in ten month time intervals which are further subdivided into one month survival time intervals as noted on the graph as a circle (o) for females and a plus (+) for males. The cumulative hazard function illustrates the probability for the outcome of interest, death and how that probability changes with time. The cumulative probability of death increased for both females and males over the time interval in Figure 15 with males having an increase in the probability of death as compared to females.

Figure 15: Cumulative Hazard Function for the Life Table Model Female and Males Lung Cancer Cases

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When examining the cumulative hazard function plot a comprehensive representation of how the hazard is changing over time cannot be completely ascertained. Survival data are not normal and therefore to view the cumulative hazard function transforming the data with a logarithmic function allows for a more complete examination of the hazard between males and females. As shown in Figure 16, after the 6transformation of the data, the hazard is shown to remain constant between males and females over the time under study (see Figure 16). This was important to examine (constant hazard) to ensure the assumption of proportionality was not violated, i.e. the probability of death was constant over the study time period between males and females.

Figure 16: Transformation of the Cumulative Hazard Function Female and Males Lung Cancer Cases

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Hypothesis II Conclusion In conclusion, for Research Question Two, the null hypothesis was rejected as there were statistically significant differences in gender specific survival. Women lung cancer cases had an increased probability of survival (a survival advantage) versus men with lung cancer. It is reported in the literature that there is a distinct survival advantage for women with lung cancer as compared to men with lung cancer. This result of increased survival for women was verified during Hypothesis II testing and analyses; these results are consistent with published literature.

Hypothesis III Women with the same histological type, stage, grade of lung cancer, and the same treatment modality differ significantly in survival as compared to men with the same histological type, stage, and grade of lung cancer, and the same treatment modality. Introduction The third aim in testing the hypothesis of this research study was to expand the investigation of lung cancer treatment differences for females versus males in order to answer the research question of whether differences in treatment assignment based on gender altered survival. For lung cancer cases, females have been shown to have a distinct survival advantage relative to males8, 15, 17. The statistical modeling approaches that demonstrate this survival advantage for females do not account for any effects due to moderating variables on the relationship or association between the independent variable

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of gender and survival. The intent was to determine under which conditions females demonstrated or did not demonstrate a survival advantage as compared to males by investigating differences in gender-specific survival for men and women lung cancer cases grouped or stratified by treatment modality, histologic type, stage, grade and other research covariates and by expanding the modeling approach to include interaction terms. The research statistical approach to test Hypothesis 3 was to employ Survival Analysis or ―Time-to-Event‖ Analysis. This technique can be applied to evaluate data that are censored (the research study event is not observed) and are not normal (lack of normality) due to censoring. Under the conditions described in this research, the lung cancer data from the eight cancer registries were right censored. Right censoring is defined as the non-observance of the study event, i.e. death, during a specified time range. During the specified time frame (1-1-2000 through 12-31-2004) under study, any non-observed event (death) would classify that individual lung cancer case as censored. As stated in Chapter Three, the model selected to examine the relationship between survival and the covariates was the Cox Proportional Hazards model. This particular model is categorized as semi-parametric as the baseline hazard is not specified but other assumptions such as time-invariant covariates across the study period are assumed. Time-invariant covariates imply that the ratio of the hazard for any two observations is similar across the period of study. Prior to hypothesis testing with the Cox Proportional Hazards statistical model, the first step to answer Research Question Three was to perform a preliminary ―Exploratory Univariate Survival Analysis‖ for the

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individual categorical independent variables. Commonly the ―Kaplan-Meier Survival Method’ is utilized but due to the large number of lung cancer cases with many failures in the data set (N =44,863) the ―Life Table Survival Method‖ was selected for the preliminary analyses3. This initial or preliminary testing was done to evaluate the survival function and shape of the survival curves for each covariate over survival time. The survival curves for the groups or strata of the individual covariates were utilized to examine the proportionality between the strata (groups) for each variable. When the groups or strata for the independent variable are proportional, the curves of the survival function graphs between the strata appear approximately parallel (the lines of the graphs do not diverge or do not cross). After evaluating the survival function curves between gender vs. survival time, stage vs. survival time, grade vs. survival time, and morphology vs. survival time for the lung cancer cases diagnosed over the 5 year study period, it was determined that utilization of the Cox Proportional Hazard model was appropriate as the proportionality assumption held for gender, stage, grade, and morphology over survival time. There were some overlapping survival curves for independent variables of age group at diagnosis, marital status at diagnosis, race, and treatment group over time which could have been problematic as the assumption of proportionality (constant hazard) could possibly be violated making the Cox Proportional Hazard model inappropriate to use with the lung cancer data set. The initial non-proportionality concern was addressed later in this section with the variables (age group at diagnosis, marital status at diagnosis, race, 3

Information from ―Analyzing Survival Data from Clinical Trials and Observational Studies‖ by Ettore Marubini and Maria Grazia Valsecchi; section 3.3.2., page 54.

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and treatment group) being tested via residual analysis (results are shown in Figures 13 and 14). During the initial phase of univariate survival analysis, the statistics generated by the ―Test of Equality over Strata‖ were used to evaluate any inequality across the strata for each independent categorical variable. The lifetest procedure generated log-rank test statistic, with the p-values (<0.0001) being significant for gender, stage, grade, morphology, race, marital status at diagnosis, age group at diagnosis, and treatment group. Table 61 contains the some of the results for the Life Table Method ―Tests of Equality over the Strata‖ and as noted above the p-values are significant. A possible limitation of the statistical analysis for this data set for the highly significant p-values may not just be a result of a true null hypothesis (no difference) but rather may be attributed to the extremely large sample size (N = 44,863) influencing the statistics.

Table 61: Life Table - Test of Equality over Strata Parameter Gender Stage Grade Morphology

Test

Chi-Square Pr > Chi-Square

Log-Rank Log-Rank Log-Rank Log-Rank

206.90 502.70 844.80 417.80

< 0.0001 < 0.0001 < 0.0001 < 0.0001

The next phase in testing Hypothesis 3 was assessing the research question by fitting the data to the Cox Proportional Hazard model which is a semi-parametric mathematical equation (the procedure in SAS is referred to as ―PROC PHREG‖). During

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the model build or selection of the covariates and interaction terms to be included in the final model, the ―Stepwise‖ procedure was utilized in a forward selection (alpha = 0.05) and backward elimination of the variable or interaction term if the significance level of 0.05 was not met. Four variables (gender, stage, grade, and morphology) were coded to remain in the model during the stepwise procedure without having to meet the entry and exit specifications of 0.05 as these were the primary variables under study. At the completion of the stepwise procedure, the final model was assessed by examining the pvalues for the main effect variables and the variable combinations for the interaction terms. Interaction terms were included in the final model so the effect of moderating variables that could impact the relationship between the independent variables and survival were identified. Below in Table 61-a, the results of the Type III testing for the final model are given. As shown Table 61-a, there were a total of six main effects variables and ten interaction terms.

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Table 61-a: The Cox Proportional Hazards Model (CPHM1) Type 3 Tests - Final Model

Effect Gender Morphology Gender * Morphology Grade Grade * Morphology Stage Stage * Morphology Age Group Stage * Age Group Race Treatment Type Gender * Treatment Type Treatment Type * Morphology Treatment Type * Grade Treatment Type * Stage Treatment Type * Age Group Treatment Type * Race

DF 1 3 3 3 9 3 9 4 12 2 7 7 21 21 21 28 14

Note: Age Group at defined on/at Date of Diagnosis * indicates interaction term

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Wald Chi-Square 34.54 17.21 8.61 3.81 29.34 92.72 20.69 64.69 22.99 4.14 20.54 23.01 73.18 61.24 147.33 104.76 34.35

Pr > ChiSq <.0001 0.0006 0.0350 0.2823 0.0006 <.0001 0.0141 <.0001 0.0278 0.1259 0.0045 0.0017 <.0001 <.0001 <.0001 <.0001 0.0018

The extracted equation or expression from the Cox Proportional Hazards model would be:

Hazard Rate = exp (1genderI + 2morphologyI + 3gradeI + 4stageI + 5age_groupI + 6raceI + 7treatment_typeI + 8 genderI * morphologyI + 9 gradeI * morphologyI + 10 stageI * morphologyI + 11 stageI * age_groupI + 12 genderI*treatment_typeI + 13 treatment_typeI * morphologyI+ 14 treatment_typeI * grade I + 15treatment_typeI* stageI + 16treatment_typeI* age_groupI + 17treatment_typeI*raceI + others)

The next three sections for Hypothesis III include 1) the interaction terms analysis, 2) the residuals analysis (model assessment), and 3) gives the results for the overall interaction effect on the probability of survival. It was important to include the interaction terms analysis so a comparison of the results could be made to the overall interaction effect on the outcome.

Section 1: Cox Proportional Hazards Model Interaction Terms In the next six tables (Tables 62- 67), the results of the final Cox Proportional Hazards Model (CPHM1) are given for the interaction terms extracted from the full model that contained statistically significant results. When there were associations between the independent variable and the outcome of interest, survival, the relationship

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varied at different levels dependent upon the effect modifier. Interaction terms included in the statistical model (when appropriate) allowed for the opportunity to examine a more complete overview of the relationship between an independent variable and the outcome and how that relationship changes based on the moderating variable (effect modifier). As stated previously during Hypothesis II testing, in the majority of the currently published literature of gender-specific survival, gender is evaluated as a main effect without interaction terms. The survival estimates based on the information presented in that literature are reported favorably for women relative to men8, 15, 17. In testing Hypothesis II, the results were consistent with the published literature that found females have a survival advantage over men. Part of the research investigation of gender-specific survival was to verify that the results obtained from the lung cancer data set were consistent with other research results published in the current literature. The investigation of gender-specific survival was expanded during Hypothesis III testing. After adjustment for covariates, the relationship between gender-specific survival and the treatment received was analyzed. As shown in the following tables (Tables 62 through 67), there were increased hazard or increase risk of death based on treatment received. The overall gender effect reported later in this section on survival will include the results for the terms containing gender; therefore the statistics for Table 62 will not be reviewed here.

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Table 62: Hazard Ratios and 95% Confidence Intervals Interaction Term of Treatment Group and Gender Extracted from the Cox’s Proportional Hazard’s Final Model (CPHM1)

Interaction Terms** Hazard Ratio 95% LCI 95% UCI Gender Treatment Group (Moderator) Female Radiation 1.18 1.09 1.29 Female Chemotherapy 1.16 1.07 1.26 Female No Treatment 1.13 1.05 1.21 Female Radiation + Surgery 1.13 0.96 1.34 Female Radiation + Chemotherapy 1.18 1.09 1.27 Female Surgery + Chemotherapy 1.11 0.93 1.33 Female Radiation + Surgery + Chemotherapy 1.07 0.92 1.25 Male* Surgery* 1.00* LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, * = designates reference ** Adjusted for morphology, gender * morphology, grade, grade * morphology, stage, stage * morphology, age group, stage * age group, race, morphology * treatment type, grade * treatment type, stage * treatment type, age group * treatment type, and race * treatment type

The results displayed in Table 63 are for the interaction term of treatment group and stage; stage is the moderator in this interaction term extracted from the full CPH model. For the first treatment group of radiation therapy, lung cancer cases with stage 3 were at a 20.0% decreased risk for death than those lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. There are no trends or overall significant findings demonstrated for an increased or decreased risk in this treatment group (radiation) based on stage 1 or stage 2 for this disease. In the case of chemotherapy (treatment group 2),

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there is a decreasing risk of death as stage increases. For stage 1 lung cancer cases receiving chemotherapy the risk of death was 1.68 times greater than those lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. Stage 2 lung cancer cases receiving chemotherapy were 1.44 times more likely to die than those lung cancer cases receiving surgery after adjustment. Later stage lung cancer cases (stage 3) receiving chemotherapy were 7.0% less likely to die than those lung cancer cases receiving surgery after adjustment, although this was not statistically significant (HR = 0.93, 95% CI 0.81 - 1.06). After controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type, stage 1 (HR = 1.68, 95% CI 1.41 - 1.99) and stage 2 (HR = 1.44, 95% CI 1.19 - 1.74) lung cancer cases were shown to be at a greater risk or hazard for death as opposed to lung cancer cases receiving surgery alone. These findings are of particular interest as clinically early stage disease is associated with a decreased hazard for death and later stage disease has decreased survival. Evaluation of the effects of the moderator (stage) in the relationship between treatment and survival after adjustment in the full model for this data set demonstrated that early stage lung cancer cases receiving chemotherapy had decreased

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survival. For the next treatment group of ―no treatment‖, early stage lung cancer cases (stage 1) that received no treatment were 16.0% more likely to die (HR = 1.16, 95 % CI 1.02 - 1.32) than those lung cancer cases receiving surgery alone after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. The risk of death increased by 46.0% for stage 2 lung cancer cases as compared to other lung cancer cases (HR = 1.46, 95 %CI 1.25 - 1.70) versus receiving surgery alone after adjustment. Although the hazard ratio decreased for stage 3 disease, the result shown in Table 63 for stage 3 lung cancer cases receiving no treatment was not statistically significant (HR = 0.93, 95% CI = 0.82 - 1.06). Once again it would be expected that later stage disease would have increased mortality; this was association was not demonstrated for those lung cancer cases in the no treatment group. The only statistically significant hazard ratio in treatment group 4 (radiation in combination with surgery) in Table 63, was for stage 3 lung cancer cases. Lung cancer cases receiving radiation plus surgery were 26.0% less likely to die than those lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. This result indicated that for

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later stage disease the treatment combination of radiation and surgery improved survival. For early stage disease (Stage 1 and 2) lung cancer cases receiving chemotherapy plus radiation did not demonstrate a decrease in the risk of death (Table 63). On the contrary, stage 1 lung cancer cases receiving this treatment combination were 43.0% more likely to die with stage 2 lung cancer cases being 31.0% more likely to die than those lung cancer cases receiving surgery after adjustment. The trend of increasing stage having a decreased risk for death for the radiation plus chemotherapy treatment group was further demonstrated as stage 3 had an 11.0% decrease in the risk for death versus lung cancer cases receiving surgery after adjustment. These results suggest that stage moderated the relationship between the treatment group and survival: earlier stage lung cancer cases have decreased survival when the treatment for the disease consists of radiation in combination with chemotherapy. For the last two treatment groups (surgery + chemotherapy and radiation + chemotherapy + surgery) in Table 63, the only significant hazard ratio was for stage 1 lung cancer cases receiving all three treatment modalities of radiation, chemotherapy and surgery (HR = 1.79, 95% CI 1.31 – 2.44) as compared to lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. This treatment group (radiation + surgery + chemotherapy) with stage 1 disease had the highest risk for death (79.0%) as compared to all the other treatment groups as shown in Table 63.

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Table 63: Hazard Ratios and 95% Confidence Intervals Interaction Term of Treatment Group and Stage Extracted from the Cox’s Proportional Hazard’s Final Model (CPHM1) Hazard Ratio

Interaction Terms**

95% LCI 95% UCI Stage Treatment Group (Moderator) Radiation I 1.03 0.89 1.19 Chemotherapy I 1.68 1.41 1.99 No Treatment I 1.16 1.02 1.32 Radiation + Surgery I 1.28 0.97 1.68 Radiation + Chemotherapy I 1.43 1.22 1.66 Surgery + Chemotherapy I 1.25 0.95 1.64 Radiation + Surgery + Chemotherapy I 1.79 1.31 2.44 Surgery* IV* 1.00 Radiation II 1.10 0.92 1.32 Chemotherapy II 1.44 1.19 1.74 No Treatment II 1.46 1.25 1.70 Radiation + Surgery II 1.07 0.82 1.41 Radiation + Chemotherapy II 1.31 1.11 1.54 Surgery + Chemotherapy II 1.01 0.72 1.41 Radiation + Surgery + Chemotherapy II 1.27 0.95 1.71 Surgery* IV* 1.00 Radiation III 0.80 0.69 0.92 Chemotherapy III 0.93 0.81 1.06 No Treatment III 0.93 0.82 1.06 Radiation + Surgery III 0.74 0.59 0.92 Radiation + Chemotherapy III 0.89 0.78 1.01 Surgery + Chemotherapy III 0.90 0.71 1.14 Radiation + Chemotherapy III 1.31 1.11 1.54 Radiation + Surgery + Chemotherapy III 0.95 0.77 1.17 Surgery* IV* 1.00 LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, * = designates reference ** Adjusted for gender, morphology, gender * morphology, grade, grade * morphology, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type

Table 64 exhibits only one statistically significant hazard ratio for the interaction term of treatment group and grade. Grade I lung cancer cases receiving chemotherapy had an increased risk of death with a hazard ratio of 1.51 (95% CI 1.12 - 2.03) relative to 236

lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade * morphology, stage, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, age group * treatment type, and race * treatment type.

For this data set and as

shown in Table 64, there was only one statistically significant hazard ratio which may have been due to chance alone versus being truly significant for the relationship between treatment modality of chemotherapy and decreased survival moderated by the grade of disease. The hazard ratios and the 95% confidence intervals for the interaction term of treatment group and morphology are displayed in Table 65. Morphology moderated the relationship between the independent variable treatment group and the outcome, survival. The finding that morphology acted as a moderator was consistent with clinical practices of treating a disease based on cell type with a particular treatment regimen. The statistics demonstrated that the treatment selection based on cell type could decrease survival or may not increase survival. There were only three treatment groups (radiation, chemotherapy, and radiation in combination with chemotherapy) with statistically significant results meaning for a particular treatment group the hazard ratio and the confidence interval for that hazard ratio did not include 1. For each of those three treatment groups, the hazard ratios demonstrated an increased risk for death or decreased survival for the lung cancer cases. Lung cancer cases with adenocarcinoma receiving radiation were 45.0% more likely to die (HR = 1.45, 95% CI 1.03 – 2.04) as compared to

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the other treatment groups with adenocarcinoma after controlling for gender, gender * morphology, grade, grade * morphology, stage, stage * morphology, age group, stage * age group, race, gender * treatment type, stage * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. The hazard ratio increased by 65% (HR = 1.65, 95% CI 1.17 – 2.33) for squamous cell lung cancer cases receiving radiation with a hazard ratio for large cell carcinoma increasing by 61% (HR = 1.61, 95% CI 1.14 – 2.28) relative to lung cancer cases receiving surgery after adjustment. For adenocarcinoma lung cancer cases receiving chemotherapy, the risk of death increased by 42.0% than those lung cancer cases receiving surgery after adjustment. The hazard ratio for squamous cell lung cancer receiving chemotherapy was 1.51 meaning there was a 51.0% increase in the risk of death as compared to lung cancer cases receiving surgery after controlling for gender, gender * morphology, grade, grade * morphology, stage, stage * morphology, age group, stage * age group, race, gender * treatment type, stage * treatment type, grade * treatment type, age group * treatment type, and race * treatment type. Radiation in combination with chemotherapy demonstrated the same trend of decreased survival for the lung cancer morphological types of adenocarcinoma, squamous cell, and large cell carcinoma. Adenocarcinoma lung cancer cases receiving radiation plus chemotherapy were 48.0% (HR = 1.48, 95% CI 1.08 – 2.02) more likely to die with large cell lung cancer cases having a 62.0% increase risk for death (HR = 1.62, 95% CI 1.18 – 2.24) than those lung cancer cases receiving surgery after adjustment. According to the statistics, chemotherapy, radiation, and chemotherapy in combination

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with radiation decreased survival for all three lung cancer types. In Table 65, the hazard ratios for radiation in combination with surgery was the one treatment group that did increase in survival (HR’s less than 1.00) for all three NSCLC types (adenocarcinoma, squamous, and large cell) as compared lung cancer cases receiving surgery but these results were not statistically significant as the 95% confidence intervals did include 1. For all other treatment groups (no treatment, surgery plus chemotherapy, and radiation in combination with surgery plus chemotherapy) listed in Table 65, the results were not statistically significant therefore an association between the treatment group and survival moderated by morphology was not demonstrated.

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Table 64: Hazard Ratios and 95% Confidence Intervals Interaction Term of Treatment Group and Grade Extracted from the Cox’s Proportional Hazard’s Final Model (CPHM1) Hazard Ratio

Interaction Terms**

95% LCI 95% UCI Grade Treatment Group (Moderator) Radiation I 1.03 0.76 1.41 Chemotherapy I 1.51 1.12 2.03 No Treatment I 1.05 0.81 1.38 Radiation + Surgery I 0.66 0.34 1.27 Radiation + Chemotherapy I 1.26 0.94 1.67 Surgery + Chemotherapy I 1.22 0.69 2.17 Radiation + Surgery + Chemotherapy I 0.92 0.5 1.71 Surgery* IV* 1 Radiation II 0.93 0.71 1.23 Chemotherapy II 1.09 0.84 1.41 No Treatment II 1.06 0.84 1.36 Radiation + Surgery II 0.89 0.52 1.53 Radiation + Chemotherapy II 1.12 0.88 1.44 Surgery + Chemotherapy II 0.97 0.6 1.59 Radiation + Surgery + Chemotherapy II 0.97 0.61 1.54 Surgery* IV* 1 Radiation III 0.92 0.7 1.2 Chemotherapy III 0.89 0.7 1.14 No Treatment III 1 0.79 1.26 Radiation + Surgery III 0.91 0.54 1.55 Radiation + Chemotherapy III 0.98 0.77 1.24 Surgery + Chemotherapy III 0.98 0.61 1.56 Radiation + Surgery + Chemotherapy III 0.82 0.52 1.28 Surgery* IV* 1 LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, * = designates reference ** Adjusted for gender, morphology, gender * morphology, grade * morphology, stage, stage * morphology, age group, stage * age group, race, gender * treatment type, morphology * treatment type, stage * treatment type, age group * treatment type, and race * treatment type

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Table 65: Hazard Ratios and 95% Confidence Intervals Interaction Term of Treatment Group and Morphology Extracted from the Cox’s Proportional Hazard’s Final Model (CPHM1) Hazard Ratio

Interaction Terms** Treatment Group

95% LCI

95% UCI

Morphology (Moderator)

Radiation Adenocarcinoma 1.45 1.03 2.04 Chemotherapy Adenocarcinoma 1.42 1.04 1.95 Adenocarcinoma 1.07 0.79 1.46 No Treatment Radiation + Surgery Adenocarcinoma 0.72 0.31 1.65 Radiation + Chemotherapy Adenocarcinoma 1.48 1.08 2.02 Surgery + Chemotherapy Adenocarcinoma 1.03 0.61 1.72 Radiation + Surgery + Chemotherapy Adenocarcinoma 1.53 0.92 2.55 Surgery* Small Cell* 1 Radiation Large Cell 1.61 1.14 2.28 Chemotherapy Large Cell 1.43 1.03 1.97 No Treatment Large Cell 1 0.72 1.37 Radiation + Surgery Large Cell 0.82 0.35 1.91 Radiation + Chemotherapy Large Cell 1.62 1.18 2.24 Surgery + Chemotherapy Large Cell 0.97 0.57 1.66 Radiation + Surgery + Chemotherapy Large Cell 1.43 0.84 2.42 Surgery* Small Cell* 1 Radiation Squamous 1.65 1.17 2.33 Chemotherapy Squamous 1.51 1.1 2.07 No Treatment Squamous 1.16 0.85 1.58 Radiation + Surgery Squamous 0.84 0.36 1.93 Radiation + Chemotherapy Squamous 1.63 1.19 2.23 Surgery + Chemotherapy Squamous 1.04 0.62 1.77 Radiation + Surgery + Chemotherapy Squamous 1.58 0.94 2.64 Surgery* Small Cell* 1 LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, * = designates reference ** Adjusted for gender, gender * morphology, grade, grade * morphology, stage, stage * morphology, age group, stage * age group, race, gender * treatment type, grade * treatment type, stage * treatment type, age group * treatment type, and race * treatment type

The hazard ratios for the interaction term of treatment group and age group in Table 66 displayed that age was a moderator in the association between the treatment received and survival. Also in Table 66, the hazard ratios did not exhibit an increasing or 241

decreasing trend between the type of treatment received and survival. For all levels of age group (4, 5, 6, and 7) lung cancer cases receiving radiation therapy as a treatment modality were at increased risk for death as compared to lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage, stage * morphology, stage * age group, race, gender * treatment type, stage * treatment type, grade * treatment type, morphology * treatment type, and race * treatment type. Lung cancer cases in age group 4 that received radiation were 1.38 times more likely to die than those lung cancer cases receiving surgery after adjustment. All other age groups, 5 (HR = 1.51, 95% CI 1.27 – 1.81), 6 (HR = 1.27, 95% CI 1.10 – 1.46), and 7 (HR = 1.19, 95% CI 1.05 – 1.36) were at increased risk for death versus lung cancer cases receiving surgery after adjustment. The risk of death increased by 22.0% for lung cancer cases in age group 5 receiving chemotherapy (HR = 1.22, 95% CI 1.03 – 1.45) versus lung cancer cases receiving surgery after adjustment (Table 66). The variation of that risk was as small as 3.0% to a maximum risk for death of 45.0%. In Table 66, the no treatment group demonstrated a trend of decreasing risk of death with the HRs ranging from 1.65 to 1.23; as the lung cancer case became older the hazard ratio decreased but this was not statistically significant. The only other treatment group with a statistically significant hazard ratio was the treatment group of radiation in combination with chemotherapy (Table 66). Lung cancer cases that received radiation in combination with chemotherapy were 31.0% more likely to die (HR = 1.31, 95% CI 1.11 - 1.56) relative to lung cancer

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cases receiving surgery after adjustment. For all age groups in the other three treatment groups of 1) radiation plus surgery, 2) surgery in combination with chemotherapy, and 3) radiation plus surgery plus chemotherapy, the risk of death or hazard ratio was not statistically significant as compared to lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage, stage * morphology, stage * age group, race, gender * treatment type, stage * treatment type, grade * treatment type, morphology * treatment type, and race * treatment type. Age group moderated the relationship between treatment group and survival with the no treatment group overall having the highest risk of death. Possible explanations of the no treatment group having the highest risk would include 1) those particular lung cancer cases did not receive one of the seven treatments for lung cancer because they were too sick for treatment or 2) the lung cancer case may have refused treatment for their disease. Of the last four treatment groups (Radiation + Surgery, Radiation + Chemotherapy, Surgery + Chemotherapy, and Radiation + Chemotherapy + Surgery) shown in Table 66, the only treatment group moderated by age group that demonstrated a statistically significant hazard ratio was for the radiation in combination with chemotherapy treatment group. Lung cancer cases in age group 5 were at a 31.0 % increased risk versus lung cancer cases receiving surgery after adjustment. The last table (Table 67) in the Hypothesis 3 section 1 lists the hazard ratios for the interaction term of treatment group and race extracted from the full model. After controlling for gender, morphology, gender * morphology, grade, grade * morphology,

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stage, stage * morphology, age group, stage * age group, gender * treatment type, morphology * treatment type, grade * treatment type, stage * treatment type, and age group * treatment type, white and black lung cancer cases receiving radiation had an increased hazard for death or decreased survival with white lung cancer cases having a 38.0% (HR = 1.38, 95% CI 1.04 – 1.84) increased risk for death and black lung cancer cases having a 27.0% (HR = 1.51, 95% CI 1.27 – 1.81) increased risk for death than other lung cancer cases receiving surgery. For lung cancer cases receiving chemotherapy, black lung cancer cases demonstrated the only statistically significant relationship between the treatment type and survival. After adjustment, black lung cancer cases receiving chemotherapy alone were 22.0% more likely to die as compared to other lung cancer cases receiving surgery alone (HR = 1.22, 95% CI 1.03 – 1.45). For white lung cancer cases receiving no treatment there was a 65.0% increase in the risk of death (HR = 1.65, 95%CI 1.28 - 2.14) and for black lung cancer cases receiving no treatment the hazard ratio was 1.60 (95%CI 1.37 - 1.87) as compared to other lung cancer cases receiving surgery after controlling for gender, morphology, gender * morphology, grade, grade * morphology, stage, stage * morphology, age group, stage * age group, gender * treatment type, morphology * treatment type, grade * treatment type, stage * treatment type, and age group * treatment type. Once again as demonstrated in Table 66 (interaction term of treatment group and age group), the ―no treatment group‖ had the highest risk of death but in this case (Table 67) race was the moderator between treatment group and survival.

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Black lung cancer cases receiving a combination of radiation and chemotherapy had a 1.31 times increase in the risk of death with a hazard ratio varying as low as 11.0% to a maximum of 56.0% (95% confidence interval of 1.11 to 1.56) versus other lung cancer cases receiving surgery after adjustment. For all other treatment groups, i.e. radiation + surgery, surgery + chemotherapy, and radiation + surgery + chemotherapy, the results were not statistically significant. Although when just evaluating the hazard ratios for radiation in combination with surgery and chemotherapy, there was a decreased risk of death for both white (HR = 0.73, 95%CI 0.39 – 1.35) and black lung (HR = 0.72, 95%CI 0.41 – 1.27) cancer cases.

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Table 66: Hazard Ratios and 95% Confidence Intervals Interaction Term of Treatment Group and Age Group Extracted from the Cox’s Proportional Hazard’s Final Model (CPHM1) Hazard Ratio

Interaction Terms**

95% LCI 95% UCI Age Group Treatment Group (Moderator) Radiation 4 1.38 1.04 1.84 Chemotherapy 4 1.1 0.84 1.43 No Treatment 4 1.65 1.28 2.14 Radiation + Surgery 4 0.93 0.56 1.54 Radiation + Chemotherapy 4 1.24 0.96 1.61 Surgery + Chemotherapy 4 0.99 0.57 1.72 Radiation + Surgery + Chemotherapy 4 0.73 0.39 1.35 Surgery* 8* 1 Radiation 5 1.51 1.27 1.81 Chemotherapy 5 1.22 1.03 1.45 No Treatment 5 1.6 1.37 1.87 Radiation + Surgery 5 1.23 0.87 1.73 Radiation + Chemotherapy 5 1.31 1.11 1.56 Surgery + Chemotherapy 5 1.05 0.67 1.65 Radiation + Surgery + Chemotherapy 5 0.72 0.41 1.27 Surgery* 8* 1 Radiation 6 1.27 1.1 1.46 Chemotherapy 6 1.1 0.95 1.28 No Treatment 6 1.53 1.35 1.73 Radiation + Surgery 6 1.04 0.77 1.41 Radiation + Chemotherapy 6 1.13 0.98 1.32 Surgery + Chemotherapy 6 1.01 0.66 1.54 Radiation + Surgery + Chemotherapy 6 0.75 0.43 1.3 Surgery* 8* 1 Radiation 7 1.19 1.05 1.36 Chemotherapy 7 1 0.87 1.15 No Treatment 7 1.23 1.1 1.38 Radiation + Surgery 7 1.07 0.8 1.44 Radiation + Chemotherapy 7 1.08 0.93 1.24 Surgery + Chemotherapy 7 0.88 0.57 1.34 Radiation + Surgery + Chemotherapy 7 0.64 0.37 1.11 Surgery* 8* 1 LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, * = designates reference Age Groups: 4 ( > 40 - < 50 years old) , 5 ( > 50 - < 60 years old), 6 (> 60 - < 70 years old), 7 ( > 70 - < 80 years old), 8 ( > 80 - < 90 years old) ** Adjusted for gender, morphology, gender * morphology, grade, grade * morphology, stage, stage * morphology, stage * age group, race, gender * treatment type, morphology * treatment type, grade * treatment type, stage * treatment type, and race * treatment type

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Table 67: Hazard Ratios and 95% Confidence Intervals Interaction Term of Treatment Group and Race Extracted from the Cox’s Proportional Hazard’s Final Model (CPHM1) Hazard Ratio

Interaction Terms**

95% LCI 95% UCI Race Treatment Group (Moderator) Radiation White 1.38 1.04 1.84 Chemotherapy White 1.1 0.84 1.43 No Treatment White 1.65 1.28 2.14 Radiation + Surgery White 0.93 0.56 1.54 Radiation + Chemotherapy White 1.24 0.96 1.61 Surgery + Chemotherapy White 0.99 0.57 1.72 Radiation + Surgery + Chemotherapy White 0.73 0.39 1.35 Surgery* Other* 1 Radiation Black 1.51 1.27 1.81 Chemotherapy Black 1.22 1.03 1.45 No Treatment Black 1.6 1.37 1.87 Radiation + Surgery Black 1.23 0.87 1.73 Radiation + Chemotherapy Black 1.31 1.11 1.56 Surgery + Chemotherapy Black 1.05 0.67 1.65 Radiation + Surgery + Chemotherapy Black 0.72 0.41 1.27 Surgery* Other* 1 LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, * = designates reference ** Adjusted for gender, morphology, gender * morphology, grade, grade * morphology, stage, stage * morphology, age group, stage * age group, gender * treatment type, morphology * treatment type, grade * treatment type, stage * treatment type, and age group * treatment type

Section 2: Residuals The next two figures are the residual plots for the independent variables versus the log of time in months (Figures 13 and 14). A residual for a ―variable‖ is defined as the difference between the actual value and the estimated value for all units or individuals given for that particular ―variable‖ contained in the model equation. These plots were generated to assess the overall fit of the Cox Proportional Hazard model to the lung cancer data. When the requirements or assumptions for the semi-parametric Cox247

proportional Hazards model are met, the model would be appropriate or correctly estimate the behavior of the data. Validating or corroborating the final Cox Proportional Hazard model results via residual analysis established that the initial non-proportional covariates (age group at diagnosis, race, and treatment group) were independent of survival time for that period under study. If the residuals exhibited a trend, e.g. increased over time for the covariates of interest, the hazard ratio or relative risk could be overestimated (overestimation could lead to inflated coefficient estimates) and those covariates would not be time-invariant across the study period. As the residual plots for the covariates over survival time for the 5 year study time did not demonstrate any trends, the use of the Cox Proportional Hazards model was appropriate. Residual analysis was also performed to evaluate the proportionality and constant hazard assumptions for the remaining covariates and interaction terms - no trends were seen with residuals and the values fell about zero (Figures 17 and 18). In Figure 17, the Schoenfeld residuals for the independent variable (gender) versus the log of survival time in months are displayed; the residuals produced during the statistical testing were weighted and smoothed over time. There is no trend of increasing or decreasing residual patterns for gender over the log of survival time meaning the requirement of time-invariance held true; the model accurately estimated the lung cancer data behavior for females and males. As noted on the x-axis for Figure 17, time was transformed into the log of survival time due to the nature of survival data (nonnormalcy) due to the effects of censoring. If the transformation of survival time was not

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done, the residual pattern could be inappropriately displayed and interpreted incorrectly.

Figure 17: Residual Testing of the Lung Cancer Distribution Gender versus the Log of Time in Months

The next figure (Figure 18) includes the Schoenfeld residuals plots for all eight independent research variables. Although the displays in Figure 18 of the residual plots are minimized as compared to the display of the single variable, gender as shown in Figure 17, the intent was to illustrate any overall trend in the residual plots for gender, stage, grade, morphology, race, age group, treatment group, and marital status versus the log of survival time. The residual patterns did not increase or decrease over time (no slope) and were centered about zero as expected; the difference on average between the estimated and actual data point for the residual should fall or be located along the zero

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axis value. There is no trend of increasing or decreasing residual patterns for the individual independent variables over the log of survival time meaning the assumption of proportionality and constant hazard was not violated and that the model accurately estimated the lung cancer data behavior for females and males.

Figure 18: Residual Testing for the Lung Cancer Distribution Independent Variables versus the Log of Time in Months

Section 3: Overall Effect of Interaction on Survival The final assessment for the overall effect on survivorship is presented in this section. Utilizing the SAS command ―contrast‖ in the PHREG model statement, the Hazard Ratios and 95% Confidence Intervals were calculated for the variables of gender,

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morphology, stage, grade, race, and treatment type and the statistically significant interaction terms. In the following equation, the variables that were evaluated for the overall effect are given. The variables given in the equation were extracted from the full Cox’ Proportional Hazards model (Table 61-a) that contained statistically significant interaction terms:

Hazard Rate = exp (1genderI + 2morphologyI + 3gradeI + 4stageI + 5age_groupI + 6raceI + 7treatment_typeI + 8 genderI * morphologyI + 9 gradeI * morphologyI + 10 stageI * morphologyI + 11 stageI * age_groupI + 12 genderI*treatment_typeI + 13 treatment_typeI * morphologyI+ 14 treatment_typeI * grade I + 15treatment_typeI* stageI + 16treatment_typeI* age_groupI + 17treatment_typeI*raceI + others)

In Table 68, the statistically significant Hazard Ratio combinations for the statistically significant interaction terms are listed by gender, morphology, stage, grade, race, and treatment type. Following Table 68, an example of the method used to calculate the Hazard Ratios for the overall effect of gender on the probability or risk for death for a given morphological lung cancer type is reviewed.

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Table 68: Overall Effect on Survival Hazard Ratio Combinations Overall Effect Variable Gender

Interaction Term Gender1*Morphology1 Gender1 and Treatment Type1

Hazard Ratios HR = exp (1 + 8) HR = exp (1 + 12)

Morphology1 and Gender1 Morphology1 and Grade1

HR = exp (2 + 8) HR = exp (2 + 9)

Grade

Grade1 and Morphology1 Grade1 and Treatment Type1

HR = exp (3 + 9) HR = exp (3 + 17)

Stage

Stage1 and Morphology1 Stage1 and Treatment Type1 Stage1 and Age Group1

HR = exp (4 + 10) HR = exp (4 + 15) HR = exp (4 + 11)

Age Group1and Treatment Type1 Age Group1 and Stage1

HR = exp (5 + 16) HR = exp (5 + 11)

Race1 and Treatment Type1

HR = exp ( + 17)

Treatment Type1 and Gender1 Treatment Type1 and Morphology1 Treatment Type1 and Grade1

HR = exp (7 + 12) HR = exp (7 + 13) HR = exp (7 + 14)

Treatment Type1 and Stage1 Treatment Type1 and Age Group1 Treatment Type1 and Race1

HR = exp (7 + 15) HR = exp (7 + 16) HR = exp (7 + 17)

Morphology

Race Treatment Type

Note: Considering only Gender 1 (female), Stage I, Grade I, Race I (white), Age Group 1 (Age Group IV (> 40 - < 50 yrs. old)), Morphology 1, Treatment Type 1(radiation) in this example.

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Evaluating the Overall Effect of Gender on Survival

From the equation below, the gender and the statistically significant interaction terms containing gender are identified (bolded). Hazard Rate = exp (1genderI + 2morphologyI + 3gradeI + 4stageI + 5age_groupI + 6raceI +  7treatment_typeI + 8 genderI * morphologyI + 9 gradeI * morphologyI + 10 stageI * morphologyI + 11 stageI * age_groupI +  12 genderI*treatment_typeI + 13 treatment_typeI * morphologyI+ 14 treatment_typeI * grade I + 15treatment_typeI* stageI + 16treatment_typeI* age_groupI + 17treatment_typeI*raceI + others) From the equation above, the following equation results when examining of the overall effect of gender on survival: Hazard Rate = exp (1genderI + 2morphologyI + 7treatment_typeI + 8 genderI * morphologyI + 12 genderI*treatment_typeI)

Gender and Morphology Female: HR (gender =1, morphologyI) = exp (1 + 2morphologyI + 7treatment_typeI + 8 * morphologyI + 12 *treatment_typeI ) Male: HR (gender =0, morphologyI) = exp (

+ 2morphologyI + 7treatment_typeI )

Subtracting male from female given morphologyI, the following equation is given as: Female: HR (gender =1, morphologyI) = exp (1 + 8 * morphologyI + 12 *treatment_typeI)

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Then looking at morphologyI for morphology 1 = 1 and morphologyI for morphology 4 = 0, the following equation is given as: Female: HR (gender =1, morphologyI = 1) = exp (1 + 8 + 12 *treatment_typeI) Female: HR (gender =1, morphologyI = 0) = exp (1 + 12 *treatment_typeI)

Note that any treatment type does not affect the outcome under the conditions as stated above. The Hazard Ratio is then calculated for females as compared to males adjusting for morphology and controlling for treatment group as: HR = exp (1 + 8) In Table 69-a the effect of gender on the probability of survival based on the morphological lung cancer type demonstrate an increase survival for all three non-small cell lung cancer types for females as compared to males controlling for treatment type. After adjustment, females versus males with large cell lung cancer are 25% more likely to survive (HR = 0.75, 95% CI 0.70 – 0.81), whereas females with squamous cell carcinoma are 18% more likely to survive as compared to males with squamous cell carcinoma (HR = 0.82, 95% CI 0.76 – 0.87). The effect of gender on the probability of survival (Table 69-b) shows an overall increase in survival given the treatment types of chemotherapy, no treatment, radiation + chemotherapy, and radiation + chemotherapy + surgery. The hazard ratios range from a minimum of 0.83 to a maximum of 0.92. Females versus males receiving radiation in combination with chemotherapy and surgery are 17% more likely to survive (HR = 0.83, 95% CI 0.72 – 0.97) and 8% more likely to die when females as compared to males 254

receive radiation plus chemotherapy after adjustment. The statistics demonstrate that for a particular treatment combination statistically significant survivorship is exhibited for females versus males.

Table 69-a: Overall Effect of Gender on Survival Hazard Ratios and 95% Confidence Intervals Gender and Gender*Morphology Gender Female

Morphology Adenocarcinoma Squamous Cell Large Cell

HR 0.76 0.82 0.75

95% LCI 0.71 0.76 0.70

95% UCI 0.80 0.87 0.81

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Gender = Male, Reference for Morphology = Small Cell Lung Cancer

Table 69-b: Overall Effect of Gender on Survival Hazard Ratios and 95% Confidence Intervals Gender and Gender* Treatment Type Gender Female

Treatment Type Chemotherapy No Treatment Radiation + Chemotherapy Radiation + Chemotherapy + Surgery

HR 0.91 0.88 0.92 0.83

95% LCI 0.85 0.82 0.86 0.72

95% UCI 0.97 0.94 0.98 0.97

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Gender = Male, Reference for Treatment Type = Surgery

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Evaluating the Overall Effect of Morphology on Survival In Tables 69-c through 69-e the overall effect of morphology on the risk of survival are given. After adjustment, the risk of death is decreased for squamous cell carcinoma lung cancer cases by 425% and 40% for large cell lung cancer cases as compared to small cell lung cancer cases that are female (Table 69-c). In Table 69-d, large cell (HR = 0.50, 95% CI 0.28 – 0.88) and squamous cell carcinoma (HR = 0.51, 95% CI 0.29 – 0.89) are at an increase risk for survival given that those patients are grade 1.

For the overall effect of morphology on the probability of survival in Table 69-e,

four of five the treatment types are statistically significant for an increased survival with the HRs ranging from a minimum of 0.44 to a maximum HR of 0.65. The only HR in Table 69-e that demonstrates a decreased survival or increase risk of death is the morphologic lung cancer type of adenocarcinoma when those patients receive radiation in combination with chemotherapy (HR = 1.24, 95% CI 1.04 – 1.49) after adjustment.

Table 69-c: Overall Effect of Morphology on Survival Hazard Ratios and 95% Confidence Intervals Morphology and Morphology *Gender Morphology Squamous Cell Large Cell

Gender Female

HR 0.59 0.60

95% LCI 0.42 0.43

95% UCI 0.82 0.83

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Morphology = Small Cell Lung Cancer, Reference for Gender = Male

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Table 69-d: Overall Effect of Morphology on Survival Hazard Ratios and 95% Confidence Intervals Morphology and Morphology*Grade Morphology Squamous Cell Large Cell Large Cell

Grade I I III

HR 0.50 0.51 0.54

95% LCI 0.28 0.29 0.32

95% UCI 0.88 0.89 0.92

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Morphology = Small Cell Lung Cancer, Reference for Grade = IV

Table 69-e: Overall Interaction Effect of Morphology on Survival Hazard Ratios and 95% Confidence Intervals Morphology and Morphology*Treatment Type Morphology Adenocarcinoma Squamous Cell Squamous Cell Squamous Cell Large Cell

Treatment Type Radiation + Chemotherapy No Treatment Radiation + Surgery Surgery + Chemotherapy Radiation + Surgery

HR 1.24 0.65 0.44 0.62 0.45

95% LCI 1.04 0.53 0.20 0.39 0.20

95% UCI 1.49 0.80 0.97 0.99 0.99

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Morphology = Small Cell Lung Cancer, Reference for Treatment Type = Surgery

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Evaluating the Overall Effect of Grade on Survival In the next two tables (Table 69-f and Table 69-g) the effect of grade on the risk of death are given based on morphology and treatment type. Grade I (HR = 0.64, 95% CI 0.49 – 0.83) and grade III (HR = 0.68, 95% CI 53 – 0.88) demonstrate an increase risk of survival for patients that have adenocarcinoma as compared to small cell lung cancer after adjustment. The HR for Grade II adenocarcinoma lung cancer cases was not statistically significant. In Table 69-g, the effect of grade given the treatment type received shows a decrease survival for grade II and III lung cancer cases receiving chemotherapy and a decrease survival for grade III patients receiving radiation in combination with chemotherapy. The risk of death ranges from a minimum HR of 1.38 to a maximum HR of 1.66. Grade II versus grade IV lung cancer patients are 1.414 times more likely to die when they receive chemotherapy (HR = 1.41, 95% CI 1.03 – 1.95) versus receiving surgery for the treatment of their lung cancer. After adjustment, for patients with Grade II lung cancer, the risk of death increases by 38% when receiving radiation in combination with chemotherapy and 66% when receiving chemotherapy (Table 69-g). Table 69-f: Overall Effect of Grade on Survival Hazard Ratios and 95% Confidence Intervals Grade and Grade*Morphology Grade I III

Morphology Adenocarcinoma Adenocarcinoma

HR 0.64 0.68

95% LCI 0.49 0.53

95% UCI 0.84 0.88

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Morphology = Small Cell Lung Cancer, Reference for Grade = IV

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Table 69-g: Overall Effect of Grade on Survival Hazard Ratios and 95% Confidence Intervals Grade and Grade*Treatment Type Grade II III III

Treatment Type Chemotherapy Chemotherapy Radiation + Chemotherapy

HR 1.41 1.66 1.38

95% LCI 1.03 1.34 1.12

95% UCI 1.95 2.05 1.69

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Treatment Type = Surgery, Reference for Grade = IV

Evaluating the Overall Effect of Stage on Survival

In Tables 69-h, 69-i, and 69-j, the statistics for the effect of stage on the probability of survival given morphology, treatment type and age group at the time of diagnosis are given. In Table 69-h, for all stages of lung cancer (stages 1 through 3) there is an increase risk of survival for the morphologic types of adenocarcinoma, squamous cell, and large cell lung cancer as compared to small cell carcinoma after adjustment. The HRs range from a minimum of 0.41 for stage 1 adenocarcinoma patients to a maximum of 0.78 for stage 2 large cell lung cancer patients. Also for all stages of cases with squamous cell lung carcinoma the risk of an increased survival ranges from an HR of 0.52 to a maximum HR of 0.61 (Table 69-h). There is an increase risk of survival for stage 1 lung cancer cases receiving six of the seven possible treatment types based on surgery as the reference group and after adjustment (Table 69-i). Stage 1 lung cancer patients versus stage 4 lung cancer patients are 57% more likely to survive when they 259

receive radiation therapy alone and 31% more likely to survive when they (stage I lung cancer patients) receive chemotherapy for the treatment of their lung cancer. Comparing the effect of stage II lung cancer patients receiving radiation therapy alone, there is a 45% increased risk of survival (HR 0.55, 95% CI 0.44 – 0.69) after adjustment. Also for stage III versus stage IV lung cancer cases, there is a 29% increased risk of survival when receiving radiation therapy alone. Noted in Table 69-i, the percent increased risk of survival decreases with increasing stage; this same trend is exhibited for the no treatment group. The HRs for the no treatment group increase from 0.48 for stage 1, 0.62 for stage II and 0.79 for stage III lung cancer cases. This can be interpreted as the percent increase in survivorship values decreases with increasing stage. For stage I lung cancer case receiving no treatment there is a 52% increased risk of survival, stage II lung cancer cases have a 38% increased risk of survival and for stage III lung cancer cases there is a 21% increased risk of survival (table 69-i). The statistically significant hazard ratios and 95% confidence intervals for the overall effect of stage on the risk of death given an age group is shown in Table 69-j. For all stage of lung cancer, there is a decrease risk of death or increased survivorship for the four age groups 4 though 7 as compared to age group 8 and after adjustment. The HRs in Table 69-j range from 0.33 to 0.67 with a trend of increasing HRs with increasing age group for each of the four age groups listed.

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Table 69-h: Overall Effect of Stage on Survival Hazard Ratios and 95% Confidence Intervals Stage and Stage*Morphology

Stage I II III I II III I II III

Morphology Adenocarcinoma

Squamous Cell

Large Cell

HR 0.41 0.47 0.43 0.52 0.61 0.55 0.67 0.78 0.70

95% LCI 0.35 0.41 0.37 0.41 0.47 0.43 0.55 0.64 0.57

95% UCI 0.47 0.55 0.50 0.67 0.78 0.71 0.81 0.95 0.86

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Morphology = Small Cell Lung Cancer, Reference for Stage = IV

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Table 69-i: Overall Effect of Stage on Survival Hazard Ratios and 95% Confidence Intervals Stage and Stage*Treatment Type

Stage I

II

III

Treatment Group Radiation Chemotherapy No Treatment Radiation +Surgery Radiation + Chemotherapy Surgery + Chemotherapy Radiation No Treatment Radiation +Surgery Radiation + Chemotherapy Surgery + Chemotherapy Radiation No Treatment

HR 0.43 0.70 0.48 0.53 0.59 0.52 0.55 0.62 0.68 0.76 0.67 0.70 0.79

95% LCI 0.36 0.58 0.41 0.39 0.50 0.39 0.44 0.51 0.50 0.61 0.48 0.60 0.69

95% UCI 0.51 0.83 0.56 0.71 0.70 0.69 0.69 0.77 0.94 0.96 0.92 0.83 0.91

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Treatment Group = Surgery, Reference for Stage = Stage IV

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Table 69-j: Overall Effect of Stage on Survival Hazard Ratios and 95% Confidence Intervals Stage and Stage*Age Group Stage 1

2

3

Age Group 4 5 6 7 4 5 6 7 4 5 6 7

HR 0.35 0.33 0.36 0.41 0.45 0.43 0.46 0.53 0.58 0.55 0.59 0.67

95% LCI 0.26 0.28 0.30 0.35 0.33 0.34 0.37 0.42 0.43 0.45 0.49 0.57

95% UCI 0.47 0.40 0.43 0.49 0.63 0.55 0.58 0.67 0.77 0.67 0.70 0.80

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Stage = Stage 4, Reference for Age Group = 8, Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

Evaluating the Overall Effect of Age Group at Time of Diagnosis on Survival In the next tables, 69-k and 69-l, the effect of age group on the risk of survival is given for treatment type and stage.

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Table 69-k: Overall Effect of Age Group on Survival Hazard Ratios and 95% Confidence Intervals Age Group and Age Group*Treatment Type

Age Group 4

5

6 7

Treatment Type Radiation Chemotherapy Radiation +Surgery Radiation + Chemotherapy Radiation + Chemotherapy + Surgery Chemotherapy Radiation +Surgery Radiation + Chemotherapy Radiation + Chemotherapy + Surgery Radiation + Chemotherapy + Surgery No Treatment

HR 0.83 0.66 0.56 0.74 0.44 0.64 0.54 0.72 0.43 0.51 1.35

95% LCI 0.69 0.57 0.35 0.65 0.24 0.48 0.33 0.55 0.23 0.28 1.05

95% UCI 0.99 0.75 0.89 0.86 0.79 0.84 0.91 0.95 0.80 0.95 1.74

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Treatment Group = Surgery, Reference for Age Group = 8, Age Groups: 4 = (> 40 < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

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Table 69-l: Overall Effect of Age Group on Survival Hazard Ratios and 95% Confidence Intervals Age Group and Age Group*Stage Age Group 4

5

6

7

Stage I II III I II III I II III I III III

HR 0.51 0.48 0.52 0.49 0.47 0.50 0.59 0.56 0.61 0.69 0.66 0.71

95% LCI 0.40 0.37 0.40 0.37 0.41 0.43 0.46 0.48 0.55 0.53 0.56 0.62

95% UCI 0.64 0.63 0.66 0.65 0.53 0.59 0.77 0.66 0.67 0.90 0.76 0.80

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Stage = Stage 4, Reference for Age Group = 8, Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

Evaluating the Overall Effect of Race on Survival

In Table 69-m, the statistically significant HRs and 95%CIs for the overall effect of race on the probability of survival given treatment type are given. For white versus other lung cancer cases, there is a 1.43 times increased risk of death when the patient receives chemotherapy and a 1.91 times increase in the risk of death when that case receives no treatment. For black lung cancer cases versus other lung cancer cases, the risk of death increases from 1.57 times when they receive chemotherapy alone to a 2.10

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times increase in the risk of death when the patient receives no treatment.

Table 69-m: Overall Effect of Race on Survival Hazard Ratios and 95% Confidence Intervals Race and Race*Treatment Type Race White Black

Treatment Type Chemotherapy No Treatment Chemotherapy No Treatment

HR 1.43 1.91 1.57 2.10

95% LCI 1.07 1.46 1.15 1.57

95% UCI 1.90 2.50 2.14 2.82

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Race = 3 (or Other), Reference for Treatment Group = Surgery

Evaluating the Overall Effect of Treatment Type on Survival

In the next five tables, the effect of treatment type received for the probability of survival given morphology, grade, stage, age group and race. There were no statistically significant HRs and 95% CIs when the overall effect of treatment on the risk of survival was evaluated for gender. For the treatment type of chemotherapy (Table 69-n), the risk of survival was increased by 48% when the patient had large cell lung cancer versus small cell lung cancer (HR = 0.52, 95% CI 0.30 – 0.91). In Table 69-o, for the treatment type effect of chemotherapy, there was a decrease in the risk of death for both grade 1 and grade 3 lung cancer. There was an increase in survivorship of 49.8% for grade 1 patients receiving chemotherapy (HR = 0.50, 95% CI 0.27 – 0.94) and a 49% increase in 266

survival for grade 3 patients receiving chemotherapy. All other treatment types did not present statistically significant HRs with 95%CIs. The effect of treatment type on the risk of death given the stage (Table 69-p) of lung cancer demonstrated an decreased risk of survival for those stage 1 cases receiving chemotherapy alone (HR = 0.51, 95% 0.28 - 0.90) but a 2.10 times increased risk for death when stage 2 lung cancer cases received radiation alone as their treatment for lung cancer. For those lung cancer patient receiving chemotherapy in age groups 5 and 7, the risk of survival increased by as much as 79% (Table 69-q). In age group 6, when the treatment was radiation therapy alone versus surgery, the risk of death increased 2.07 times (HR = 2.07, 95% CI 1.04 – 4.13) after adjustment. In the last table (Table 69-r), the overall effect of treatment type on the risk of survival given raceI, shows that for black and white lung cancer cases, when the treatment was chemotherapy the risk of death decreases by as much as 77%.

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Table 69-n: Overall Effect of Treatment Type on Survival Hazard Ratios and 95% Confidence Intervals Treatment Type and Treatment Type*Morphology Treatment Type Chemotherapy

Morphology Large Cell

HR 0.52

95% LCI 0.30

95% UCI 0.91

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Treatment Group = Surgery, Reference for Morphology = Small Cell Lung Cancer

Table 69-o: Overall Effect of Treatment Type on Survival Hazard Ratios and 95% Confidence Intervals Treatment Type and Treatment Type*Grade Treatment Type Chemotherapy Chemotherapy

Grade I III

HR 0.50 0.51

95% LCI 0.27 0.28

95% UCI 0.94 0.94

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Treatment Group = Surgery, Reference for Grade = Grade IV

Table 69-p: Overall Effect of Treatment Type on Survival Hazard Ratios and 95% Confidence Intervals Treatment Type and Treatment Type*Stage Treatment Type Chemotherapy Radiation

Stage I II

HR 0.51 2.10

95% LCI 0.28 1.08

95% UCI 0.90 4.06

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval, Reference for Treatment Group = Surgery, Reference for Stage = Stage 4

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Table 69-q: Overall Effect of Treatment Type on Survival Hazard Ratios and 95% Confidence Intervals Treatment Type and Treatment Type*Age Group Treatment Type Chemotherapy Radiation Chemotherapy

Age Group 5 6 7

HR 0.53 2.07 0.45

95% LCI 0.29 1.04 0.21

95% UCI 0.98 4.13 0.97

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Stage = Stage 4, Reference for Age Group = 8, Age Groups: 4 = (> 40 - < 50 yrs), 5 = (> 50 - < 60 yrs), 6 = (> 60 - < 70 yrs), 7 = (> 70 - < 80 yrs), 8 = (> 80 - < 90 yrs)

Table 69-r: Overall Effect of Treatment Type on Survival Hazard Ratios and 95% Confidence Intervals Treatment Type and Treatment Type*Race Treatment Type Chemotherapy Chemotherapy

Race White Black

HR 0.41 0.53

95% LCI 0.23 0.38

95% UCI 0.75 0.75

Note: HR = Hazard Ratio, LCI = Lower Confidence Interval, UCI = Upper Confidence Interval Reference for Race = 3, Reference for Treatment Group = Surgery

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Hypothesis III Conclusion After evaluating the results generated during Hypothesis 3 testing, the decision was made to reject the null hypothesis because statistically significant differences existed in survival between women and men after controlling for covariates and interaction terms. The final CPH model included stratification based on gender, stage, grade, morphology and treatment type as well as investigating main effects and the effect of moderating variables on the association between independent variables and survival. The additional information obtained by utilizing a statistical model that included interaction terms served to reveal increased hazard ratios that demonstrated female and male lung cancer cases had survival patterns that were moderated by the treatment type received. When the overall effect was examined, survival differences were exhibited. Females as compared to males had an increased risk of survivorship specific to different treatment types and lung cancer type. Of all the treatment groups, the greatest increased survival was for women versus men being treated with radiation in combination with surgery and chemotherapy (HR = 0.83, 95% CI 0.72 – 0.98). The hazard ratios based on the gender effect demonstrated an increase in survivorship for females versus males. But when the hazard ratios for the interaction term for gender and treatment type received were examined, females were at a decreased survival by as much as 18%. Without consideration of the overall effect of gender this survival advantage would not have been identified and the results could have been misinterpreted. In conclusion, female lung cancer cases have been reported in the literature to

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have increased survival as well as decreased survival as compared to males with lung cancer. Without the modeling approach presented in this research, specific treatment regimens and the importance of that treatment on survivorship would not have been ascertained for females as compared to males. The answer to the research question of ―Does the lung cancer treatment received impact gender-specific survival‖ has been presented for this data set. The research results found that gender does play a role in some of the lung cancer treatment selected and that selection impacts gender specific survival.

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CHAPTER V: DISCUSSION Introduction Chapter 5 summarizes the results of this research with 1) an assessment of the major findings for the statistical analyses of the three hypotheses tested, 2) comparison of the key findings with current literature for consistency 3) an evaluation of the key findings for inconsistencies with current literature, 4) a review of the strengths and the weakness of the research study, 5) a presentation of the significant research findings as it relates to the importance in Public Health and lastly, 6) and future directions. The purpose of this research was to investigate if any differences in lung cancer treatment received were based on gender, and whether any associated treatment differences impacted gender-specific survival. To examine the relationship between gender-specific treatment and survival, the first research question to answer was if differences in lung cancer treatment (the outcome variable) existed based on gender (an independent variable). The question of gender-specific lung cancer treatments was important to address as a first step in the investigation as there are no published quantitative results that show whether there is a statistically significant difference regarding the lung cancer treatment received by women as compared to men12, 40-45. The selection of a particular lung cancer treatment is a clinical decision based on standardized recommendations which considers several parameters including morphologic type, stage, and grade of lung cancer4, 9, 13. Each morphologic type of lung cancer has its own medical intervention that can include any combination of surgery, radiation therapy, and/or chemotherapy. A

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particular lung cancer treatment may vary depending upon other differences such as comorbidities or regional differences, e.g. physician preferences, physician training, insurance.

Assessment of the Major Findings The procedures and methods presented in this research concerning genderspecific treatment differences, to the author’s knowledge, have not been published in the literature. Any differences in lung cancer treatment for females relative to males were important to ascertain as those differences could affect survival. The research questions were designed to investigate if gender-specific treatment differences changed the relationship between female survival and male survival for the data analyzed in this dissertation. A novel approach was used to evaluate the overall interaction effect and that impact on the treatment received and survival. To the author’s knowledge this approach has not been published in the literature as it pertains to lung cancer treatment and survival. For example, the overall gender effect was calculated from the beta coefficients for the main effect of gender plus the beta coefficients of the statistically significant interaction term containing gender. When the beta coefficients were added and exponentiated the Odds Ratios for treatment received and Hazard Ratios for survival were generated. From this information, differences in specific lung cancer treatments and gender specific survival could be ascertained. Upon examination of the Odds Ratios from the Multinomial Logistic Regression

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model, it was determined that the recommended standard of care for the treatment of primary lung cancer was not always adhered to for females versus males. For example, the ORs were statistically significant for stage I female lung cancer cases; those patients had as much as a 2.78 times increased probability of receiving radiation in combination with chemotherapy versus the standard of care, surgery. Surgery is the primary modality for the treatment of early stage lung cancer (stage I); radiation plus chemotherapy is an adjunct or secondary treatment and the treatment modality could vary based on gender. When the hazard ratios were assessed some of the treatment decisions for the lung cancer cases affected males’ survival by increasing their risk for death relative to females. Depending upon the treatment received and morphologic lung cancer type, males as compared to females could have an increased hazard for death with the hazard ranging from 8 % to 29%. These results demonstrated that males had a statistically significant decrease in survival when the overall gender effect was taken into account. Without the statistical approach utilized in this research, misinterpretation of gender specific treatment and survival could have been made. Evaluating just the main effects and/or interaction effects can give results that conflict with the overall effect a variable contributes to the outcome.

Hypothesis I Hypothesis I stated that there was a treatment difference based on gender when adjusted for the research covariates. The outcome variable of interest was treatment

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group which included eight different lung cancer treatment options. The eight lung cancer treatment groups were classified as 1) radiation therapy, 2) chemotherapy, 3) no treatment received, 4) radiation plus surgery, 5) radiation in combination with chemotherapy, 6) surgery plus chemotherapy, 7) radiation plus chemotherapy plus surgery and 8) surgery. The statistics for the data analyzed in this dissertation demonstrated that some females as compared to males depending upon stage and marital status did not receive the same lung cancer treatment modality. The standard of care as outlined in the article by Collins, et. al. (2007) is to treat a later stage lung cancer with chemotherapy or a combination of chemotherapy and radiation. Treatment decisions are based on the standards of care established by the medical community are overseen by several organizations such as the American Medical Association, the American College of Surgeons, the National Cancer Institute, and the American College of Radiology 33. The statistical analyses found that some of the treatment selection for stage I and III was gender dependent. The null hypothesis of no differences in treatment outcomes between men and women was rejected as there were statistically significant differences between gender and the lung cancer treatment received.

Hypothesis II The major finding for Hypothesis II (there was a statistically significant

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difference in survival for female lung cancer cases as compared to the survival for male lung cancer cases) was the unadjusted gender-specific survival patterns were comparable to the published literature 14-17. Female lung cancer cases had an increased probability of survival (increased survivorship) as compared to males. The mean survival time for females was 19.8 months whereas the mean survival time for males was 16.4 months; females for this data set ―on average‖ lived approximately 3.0 months longer than the males; these results were statistically significant. Women were at a lower risk to experience the event (death) as compared to men for the selected five year time range under study. Comparing these results to the literature, Ouellette et. al. (1998), ―Lung Cancer in Women as Compared to Men: Stage, Treatment, and Survival‖ 8, also found gender differences in survival. Women were found to live, on average, 12 months longer than men. The authors concluded there was a significant survival difference between men and women with lung cancer with women having a survival advantage over men. For the data analyzed in this dissertation, the null hypothesis of no difference was rejected as it was determined that there was a statistically significant increase in survival in women with lung cancer as compared to the survival of men with lung cancer.

Hypothesis III Univariate and multivariate survival analysis were included in the statistical methods to test Hypothesis Three. Hypothesis III stated that women with the same histological type, stage, grade of lung cancer, and the same treatment modality differ

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significantly in survival as compared to men with the same histological type, stage, and grade of lung cancer, and the same treatment modality. The gender effect demonstrated a decreased risk of death for females versus males dependent upon the treatment received and the morphologic lung cancer type. After adjustment, females versus males with large cell lung cancer could exhibit a 30% increase in the probability of survival (HR = 0.75, 95% CI 0.70 – 0.81) and a 29% increase in survival for females versus males with adenocarcinoma. Based on the statistical analyses with the overall gender effect, females exhibited a distinct survival advantage when the type of treatment received and morphological lung cancer type was examined. The majority of the literature17, 27, 40 that was reviewed males are at increased risk of death as compared to females with lung cancer. The cited articles do not take into account any effect of gender on survival. Comparisons were made to the statistics generated by models cited in the literature that did not mention any adjustment for interaction terms demonstrated females had a survival advantage as compared to males17, 27, 41. For example, in the article by Ringer, et. al. (2005), the statistics used for the primary outcome of survival were given as the ChiSquare and Student t test. Although survival rates (%) were given for lung cancer patients by stage of disease, histologic type, and by gender, there was no mention of interaction. Fu, et. al. (2007) 12 reported on a model that included interaction terms; the only interaction term that was statistically significant was gender and age. The authors found that women and men that were age 50 or greater demonstrated increased survival as compared to women less than 50 years of age.

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For Hypothesis III, the null hypothesis of no difference was rejected as the results of the statistical testing supported statistically significant differences in gender specific survival.

Comparison and Consistency of Key Findings with the Current Literature When comparing the characteristics of the lung cancer cases, Radzikowska, et. al., (2002), investigated demographic factors (gender, age, and smoking) and factors connected with the disease (histology, performance status, stage, treatment and survival) for lung cancer patients. Women were found to be more likely to have adenocarcinoma and SCLC as compared to men. Squamous cell cancer was the predominant type of lung cancer among men, and less than ten percent of men had adenocarcinoma. This was consistent with research findings of this dissertation, Kowski, et al., (2010); adenocarcinoma was the most prevalent histological type for women (17.8%) whereas squamous cell lung cancer included the greatest number of males (19.4%). Radzikowska, et. al., (2002) found that 21.6% of all females had adenocarcinoma of the lung. There was a 2.2% difference for the number of females with adenocarcinoma when both lung cancer data sets were compared. Fu, et. al. (2007)12 evaluated the survival rates for men and women who received one of five treatment groups (surgery alone, radiotherapy alone, surgery + radiotherapy, no surgery or radiotherapy, unknown) utilizing the life table method. With this method, for patients receiving surgery as part of their treatment, females had an increased survival

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as compared to men12. For females and males that underwent surgery in combination with radiation therapy, females had an increase in survival by as much as 66%. This increase in survivorship for women receiving radiotherapy in combination with surgery was also demonstrated by Kowski, et. al. (2010) with the Cox Proportional Hazards model. Women as compared to men receiving radiotherapy in combination with surgery were 1.08 times more likely to survive (HR = 0.92, 95% CI 0.86 – 0.98). Although each of the authors utilized different statistical methods, both found similar results of a decrease survivorship for men versus females when treatment type was evaluated. There were other areas of agreement (consistency) in this research with cited literature8, 15, 17, 27, 167 that reported women with lung cancer survive longer than men with lung cancer. Analyses of data among females demonstrated statistically significant increased survivorship in the unadjusted survival rates as compared to males utilizing the Life Table method (non-parametric). Lung cancer mortality rates are higher in men as compared to women 3, 10. This was consistent with the research findings during Hypothesis II testing.

Comparison of the Key Findings with the Current Literature for Inconsistency The article, Ouellette, et. al. (1998), ―Lung Cancer in Women as Compared to Men: Stage, Treatment, and Survival‖ 8, found gender differences in survival were not significant. This was inconsistent with the statistical analyses addressing Hypothesis II in this research which found females having a survival advantage. When stratified analysis

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based on stage of disease was assessed by Ouellette, et. al. (1998); women were found to live, on average, 12 months longer than men. The authors concluded there was a statistically significant survival difference after adjusting by stage between men and women with lung cancer with women having a survival advantage over men. In comparison to the increased survivorship of women after adjustment for stage, published by Ouellette, et. al. (1998), this dissertation, Kowski, et. al. (2010) did not find a statistically significant relationship between gender, stage and survival when the overall gender effect was considered. These differences in survival were demonstrated utilizing a semi-parametric statistical model - the Cox Proportional Hazards model. When the statistics were assessed for the overall gender effect on survival, there were no statistically significant hazard ratios that included gender and stage. Ringer, et al. (2005) in the article "Influence of sex on lung cancer histology, stage, and survival in a Midwestern United States Tumor Registry." identified differences between men and women with regard to lung cancer type, stage at diagnosis, and survival. Women were found to have a decreased survival with late stage lung cancer as compared to men 27 but there was no expansion of the results based on any analysis that included the type of treatment received for women and men. Kowski, et. al. (2010) research results for the lung cancer cases demonstrated an inconsistency based on the treatment received. Females versus males with lung cancer were at a statistically significant increased risk of survival when they were treated with radiation in combination with surgery (HR = 0.92, 95% CI 0.86 – 0.98), chemotherapy alone (HR =

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0.91, 95% CI 0.85 – 0.97), radiation in combination with chemotherapy and surgery (HR = 0.83, 95% CI 0.72 – 0.97) or if no treatment was received (HR = 0.88, 95% CI 0.82 – 0.94) as compared to receiving surgery alone. Survival rates were shown to be independent of lung cancer morphology as cited in the article by Visbal, et. al. (2004)17. The survival rates presented in this dissertation demonstrated a statistically significant difference for the 4 major lung cancer morphologic types. When the gender effect was considered, females versus males with squamous cell lung cancer receiving surgery alone demonstrated an increase risk of survival (HR = 0.82, 95% CI 0.76 – 0.87). Also, females versus males with large cell lung cancer receiving radiation therapy alone were 1.11 times more likely to survive (HR = 0.89, 95% CI 0.82 – 0.96). This research expanded the investigation to include the possible effect of stage, grade, treatment type, age group, marital status, and race for each morphologic lung cancer type. The article ―Women and Lung Cancer: Epidemiology, tumor biology, and emerging trends in clinical research‖ by Belani, et.al. (2007), noted gender specific differences in cancer prognosis 41. Belani compared several studies examining histological types of lung cancer and gender differences. For example, the authors noted that the major histologic type of lung cancer was adenocarcinoma with ratio between males to females being 1.0 to 1.341. Belani, et. al. (2007) further reported that males as compared to females have a greater proportion of squamous cell carcinoma approximately 1.7 to 1.0. As reported in the previous section, adenocarcinoma was the

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most prevalent histological type for females (17.8%) whereas squamous cell lung cancer included the greatest number of males (19.4%). The difference in the distribution for the morphologic types could have resulted from differences in the data analyzed in this dissertation for Kowski, et. al. (2010) versus the data sets Belani, et. al. (2007) examined as those lung cancer cases were from different studies.

Study Limitations Different types of bias or systematic error can be initiated in the design phase, the data collection phase, the analysis phase or during the publication phases for the research study. Several possible limitations in this research were experienced in the initial phase of data collection. The data that were collected was secondary data. Secondary data can be subject to measurement error. This bias could have been introduced by errors made during data collection by the cancer registries. As the cancer registries collected the data in a standardized format, this particular limitation was considered to be minimal. Initially, all cancer registries that were members of NAACCR from the four geographic regions of the US were possible candidates for inclusion into the study. From the NAACCR cancer registries, cancer registries were selected that met and maintained quality standards for the years of study (2000 – 2004). Once it was established that a cancer registry followed the standardized procedures for NAACCR, two state cancer registries were randomly selected from each geographic region. Also, there were differences for each state cancer registry IRB protocol for the release of data. This may

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have introduced a selection bias in that some of the states that were selected randomly from each geographic region would not release the data; another state had to be selected from the region. Selection bias is minimal when the samples are selected randomly and although this was the intent for this research, a completely random selection of the cancer registries that supplied lung cancer cases for the data set was not achievable; this may have limited the external validity of the study results due to a systematic or random error bias. Another limitation was the limited access over which variables could be obtained from the cancer registries. Patient anonymity was a major concern limiting the number of variables that could be obtained for the research study. Also variations due to changes in the characteristics of the lung cancer population may have been introduced by geographic differences, e.g. different patterns of care specific to a region, environmental differences, e.g. an increase in lung cancer cases due to radon and these random variations may have limited the interpretation of the study results. When evaluating the statistical analyses, one of the limitations could be identified as some of the lower bounds of the confidence intervals were minimally statistically significant, i.e. some of the lower bounds of the confidence intervals did approach one. The statistics were reported and standardized on the level of significance to the hundreds; therefore these results were still reported as statistically significant. Not all subgroup analyses resulted in statistically significant findings, which could be interpreted as a possible limitation if statistically significant results for all subgroups were anticipated.

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Without the subgroup analyses, the information on treatment and gender specific survival differences between could not be determined or examined. One of the major limitations for comparative purposes was that the current literature8, 15, 17, 27, 167 does not addressed treatment differences based on gender. Contrary to the current literature, gender specific survival differences, were demonstrated when females and males were stratified by lung cancer type (morphology), stage, grade and treatment type and when the effect of moderating variables were accounted for in the statistical models. The authors Belani, et. al. (2007) expressed an urgent need to increase research and funding to improve lung cancer care, in particular for women 41, their recommendation was based on limited information as difference in treatment modality by gender only included studies focused on surgery alone or radiation therapy alone. For this research presented in this dissertation, the major treatment types for lung cancer were critical for a valid assessment. As demonstrated in the statistical analyses, there are statistically significant differences in the treatments women receive as compared to men based on stage for the data analyzed in this dissertation.

Study Strengths The data from the eight cancer registries for this research was acquired over a year and a half time period. Strength in the study design included the large population size (power) and quality of data. Each cancer registry that was included in the data set met national standards as outlined by NAACCR decreasing any discrepancies with data

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collection and data quality. In the design phase, strength of this study was in the protocol for the selection of the state cancer registries which minimized selection bias by the random selection of the cancer registries. Error due to random variations in the characteristics of lung cancer cases was accounted for by assessing random effects during the statistical testing phase. Possible random variations in the lung cancer cases due to geographic or environmental differences that may have invalidated the results were addressed comparing a random effects model to a fixed effects model. No effect on the association between for the outcome and independent variables were seen for the data analyzed in this dissertation when the two models were compared. A particular strength of the study statistical testing included a more complete assessment of gender-specific survival adjusted for treatment type, stage, morphology, grade and interaction terms. Studies have not been published in the literature (to the author’s knowledge) utilizing a statistical modeling approach which included these variables with interaction terms. Temporal differences due to changes in treatment regimens for the treatment of lung cancer were minimized as the time range of this study was 5 years (01-01-2000 though 12-31-2004), as the standards of care did not change over this time period. Also there were no coding changes introduced by NACCR for lung cancer during the study time range, so any misclassification error would be thought as minimal. Expanding upon the strength of the methods utilized, initially, during the model criteria development, different classifications (strata) for lung cancer treatments were identified. Other independent variables were selected for inclusion into the statistical

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model (the Multinomial Logistic Regression model) to answer question one. Stratification based on the independent variables of gender, morphology, stage, grade, age, race and marital status was utilized in the statistical model but this approach is unlike statistical models in other currently published studies12, 40-45. This research model identified possible moderating variables that could have affected a lung cancer treatment based on the overall gender effect. The fixed effects were accounted for in the first model for the multinomial logistic regression model (MLR1). Another important aspect in answering Research Question I was to investigate possible random effects. A random effects component was included in a second multinomial logistic regression (MLR2) model. The decision to test for possible changes in the associations between the outcome and independent variables due to random effects was based on previous risk factors cited in the literature (Chapter Two) which included the environment10, 172 and geographic variations10. Possible random effects due to these and other risk factors may have introduced differences in the lung cancer cases from the state cancer registries located in the four geographic regions of the United States. Any differences in the association between the outcome and covariates due to the fixed effects versus random effects would have to be identified as the resultant statistics could be biased and could have included invalid interpretations. After the assessment of gender-specific treatment differences as outlined in Question One during Hypothesis I testing, Research Questions Two and Three then expanded the study of survival based on gender differences and other covariates. Other

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covariates included the treatment received, age, morphologic lung cancer type, grade and stage. Research Question Two examined survival rates between males and females without any statistical adjustment for additional covariates in the model. The investigation of the unadjusted lung cancer survival for the data analyzed in this dissertation over the five year time interval served a two-fold purpose. First, an initial assessment of the unadjusted gender-specific survival associated with these data had to be made without the effects of the covariates on the outcome. Secondly, an evaluation of the survivorship for these data was necessary so a comparison of the gender specific survival patterns reported in the literature could be made. In the literature, females with lung cancer have been reported to have a survival advantage relative to males with lung cancer3, 10; consistency with the published literature would add to the external validity of the findings for this research. For example, although interpretations of the unadjusted results were limited in scope, the individuals comprising the research lung cancer data set could be representative of the lung cancer cases in the US if the lung cancer data set survival patterns were consistent with gender-specific lung cancer survival results published in the literature3, 10. Other study’s strengths in the methodology to answer to the final research question during Hypothesis III statistical testing utilized univariate and multivariate survival analysis. Univariate survival analysis was comprised of evaluating the statistics and graphs generated during the non-parametric technique for the Life Table Method. Each independent variable was tested separately to evaluate the proportionality

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assumption between the strata of the independent variable versus survival time. The proportionality assumption infers that the hazard or risk of failure (death) is constant over survival time. For the assumption of proportionality to hold true, the graphs between the survival curves and the strata of the independent variable will be parallel; any overlapping, diverging or converging lines of the graphs can be cause for concern as this could violate the basic statistical assumptions for a semi-parametric model. The semi-parametric method, the Cox’s Proportional Hazards model, was selected to assess the multivariable relationship between the outcome (survival time) and the independent variables with the inclusion of interaction terms. In order to obtain a model with the inclusion and exclusion of variables and variable combination for second order interaction terms, the stepwise procedure was used. Included in the evaluation of the model fit, any non-proportionality concerns for a non-constant hazard over survival time were addressed via residual analysis. Residual analysis was used to test for trends; any resultant trends in the residual plots for the individual variables would be displayed as increasing or decreasing slopes over the log of survival time. If a trend was displayed for a variable over the log of survival time, the model would be inappropriate for the variable selected or the model would ―not fit the data‖ properly as the associated hazard for the variable was not constant over the survival time. The reported relationship in the literature between gender and survival is inconsistent. Contrary to some of the articles published in the literature8, 15, 17, 27, 167 , with women having increased survivorship as compared to men, in some circumstances

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this research found female had a survival disadvantage as compared to males. Treatment differences based on gender were demonstrated and that those treatment differences changed the association for gender-specific survival when adjustments for the covariates and interaction terms were taken into account in the model. When the overall gender effect was considered for the treatment received, morphologic lung cancer type and survival, this research design and resultant findings supports the literature27 in which females have an increased survivorship as compared to men.

Public Health Importance Finding the most effective treatment for increasing lung cancer survival has immense public health consequences. Finding the most effective treatment includes many factors that must be accounted for but can be difficult to ascertain. Prior to investigating effective treatments that increase survival, the examination of treatment differences based on key factors for lung cancer would have to be made. This would include any treatment for lung cancer that differed on the basis of gender. The clinical pathways for the care of lung cancer patients is standardized but when quantifiable techniques were utilized, differences in the standard of care for lung cancer patients were demonstrated to be gender dependent. For example, the standard of care for early stage lung cancer is surgery. For this data set, surgery was not consistently shown to be the first treatment choice for early stage lung cancer. For example, separated females with stage I lung cancer versus separated males with stage I were 2.82 times more likely to

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receive chemotherapy alone (OR = 2.82, 95% CI 1.17 – 6.80) as compared to receiving surgery alone. For later stage disease, divorced females as compared to divorced males with stage III lung cancer were 1.57 times more likely to receive radiation in combination with surgery and chemotherapy (OR = 1.57, 95% CI 1.07 – 2.30) versus receiving surgery alone. For later stage disease, radiation therapy in combination with chemotherapy or chemotherapy alone is the standard treatment recommendation. Building on this information of gender differences in lung cancer treatments, when the overall gender effect was assessed for survival, lung cancer type and the treatment received, males versus females had a statistically significant decrease in survival. Gender specific survivorship was demonstrated to be statistically significant when adjusted for grade, grade*morphology, stage, stage*morphology, age group, stage*age group, race, treatment type*morphology, treatment type*grade, treatment type*stage, treatment type*age group, and treatment type*race. When the gender effect for survival was assessed, females compared to males had a statistically significant survival advantage for six of the seven treatment groups. For the other treatment group of radiation therapy in combination with chemotherapy, the result for the gender effect on survival was not statistically significant. Generally, lung cancer cases receive a specific treatment for lung cancer regardless of gender; this was not the case for the data analyzed for this dissertation. For males versus female lung cancer cases, differences in the type of treatment received could increase the risk of death or decrease survival time. The associated gender differences with treatment selection were tested with multiple

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modeling techniques resulting in the same conclusion, there is a statistically significant difference in the way female and male lung cancer cases are treated. The methods and statistical analyses outlined in this research identify the impact of treatment decisions on female and male survival in particular for early stage lung cancer. The costs associated with lung cancer care are enormous according to the National Heart Lung & Blood Institute (NHLBI). Lung cancer costs shows medical expenditures as approximately 10 billion annually, according to the Centers for Medicare and Medicaid Services (CMS) 115. Over 13% of the total cancer care costs for 2006 were attributed to lung cancer. The non-medical total or personal care exceeded 250 billion for the same time period. If it is possible to assess the most effective treatment, there could be an increase in survival and a decrease in healthcare costs, thereby improving Public Health.

Future Directions A first step in the future direction of this research would be a comparative analysis of an active versus passive cancer registry such as SEER. These data are collected and compiled independently by SEER registries. Further, the data are publicly available and issues of patient confidentiality will be minimized. An independent comparison and verification of the study results would be a necessary next step to verify that treatment differences based on gender exist. Lastly, a possible future direction, after validation of the research results presented in this dissertation would be the development

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of a task group to investigate treatment differences based on gender and the subsequent impact on gender specific survivorship. Several scientific and medical associations such as the American Medical Association, the American College of Surgeons, the National Cancer Institute, or the American College of Radiology might possibly accept this role.

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APPENDICES

310

Appendix I: State Demographics Table 70: Geographic Area: Florida Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

15,982,378

100.0

SEX AND AGE Male Female

7,797,715 8,184,663

48.8 51.2

Under 5 years

945,823

5.9

5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

1,031,718 1,057,024 1,014,067 928,310 2,084,100 2,485,247

6.5 6.6 6.3 5.8 13.0 15.5

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over

2,069,479 821,517 737,496 1,452,176 1,024,134 331,287

12.9 5.1 4.6 9.1 6.4 2.1

38.7

(X)

18 years and over Male Female 21 years and over

12,336,038 5,926,729 6,409,309 11,736,378

77.2 37.1 40.1 73.4

62 years and over 65 years and over Male Female

3,245,806 2,807,597 1,216,647 1,590,950

20.3 17.6 7.6 10.0

Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000. 311

Table 71: Geographic Area: Idaho Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

1,293,953

100.0

SEX AND AGE Male Female

648,660 645,293

50.1 49.9

Under 5 years

97,643

7.5

5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years 45 to 54 years

100,756 104,608 110,858 93,994 169,433 192,968 170,248

7.8 8.1 8.6 7.3 13.1 14.9 13.2

60,024 47,505 75,970 51,889 18,057

4.6 3.7 5.9 4.0 1.4

33.2

(X)

18 years and over Male Female 21 years and over

924,923 458,934 465,989 860,220

71.5 35.5 36.0 66.5

62 years and over 65 years and over Male Female

173,097 145,916 64,161 81,755

13.4 11.3 5.0 6.3

55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

312

Table 72: Geographic Area: Indiana Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

6,080,485

100.0

SEX AND AGE Male Female

2,982,474 3,098,011

49.0 51.0

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

423,215 443,273 443,416 453,482 425,731 831,125 960,703

7.0 7.3 7.3 7.5 7.0 13.7 15.8

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over

816,865 294,169 235,675 395,393 265,880 91,558

13.4 4.8 3.9 6.5 4.4 1.5

35.2

(X)

18 years and over Male Female

4,506,089 2,174,756 2,331,333

74.1 35.8 38.3

21 years and over 62 years and over 65 years and over Male Female

4,221,426 888,688 752,831 303,797 449,034

69.4 14.6 12.4 5.0 7.4

Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

313

Table 73: Geographic Area: Massachusetts Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

6,349,097

100.0

SEX AND AGE Male Female

3,058,816 3,290,281

48.2 51.8

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

397,268 430,861 431,247 415,737 404,279 926,788 1,062,995

6.3 6.8 6.8 6.5 6.4 14.6 16.7

873,353 310,002 236,405 427,830 315,640 116,692

13.8 4.9 3.7 6.7 5.0 1.8

36.5

(X)

18 years and over Male Female

4,849,033 2,289,671 2,559,362

76.4 36.1 40.3

21 years and over 62 years and over 65 years and over Male Female

4,587,935 997,277 860,162 341,539 518,623

72.3 15.7 13.5 5.4 8.2

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

314

Table 74: Geographic Area: Nebraska Profile of Sex and Age Characteristics 2000 Subject

Number

Percent

Total population

1,711263

100.0

SEX AND AGE Male Female

843,351 867,912

49.3 50.7

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

117,048 123,445 128,934 134,909 120,331 223,273 263,834

6.8 7.2 7.5 7.9 7.0 13.0 15.4

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over

225,754 77,584 63,956 115,699 82,543 33,953

13.2 4.5 3.7 6.8 4.8 2.0

35.3

(X)

18 years and over Male Female

1,261,021 612,965 648,056

73.7 35.8 37.9

21 years and over 62 years and over 65 years and over Male Female

1,180,859 269,893 232,195 95,630 136,565

69.0 15.8 13.6 5.6 8.0

Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

315

Table 75: Geographic Area: Oregon Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

3,421,399

100.0

SEX AND AGE Male Female

1,696,550 1,724,849

49.6 50.4

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

223,005 234,474 242,098 244,427 230,406 470,695 526,574

6.5 6.9 7.1 7.1 6.7 13.8 15.4

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over

507,155 173,008 131,380 219,342 161,404 57,431

14.8 5.1 3.8 6.4 4.7 1.7

36.3

(X)

18 years and over Male Female

2,574,873 1,262,405 1,312,468

75.3 36.9 38.4

21 years and over 62 years and over 65 years and over Male Female

2,429,348 513,663 438,177 186,477 251,700

71.0 15.0 12.8 5.5 7.4

Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

316

Table 76: Geographic Area: Rhode Island Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

1,048,319

100.0

SEX AND AGE Male Female

503,635 544,684

48.0 52.0

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

63,896 71,905 71,370 75,445 71,813 140,326 170,310

6.1 6.9 6.8 7.2 6.9 13.4 16.2

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over

141,863 49,982 39,007 73,684 57,821 20,897

13.5 4.8 3.7 7.0 5.5 2.0

36.7

(X)

18 years and over Male Female

800,497 376,436 424,061

76.4 35.9 40.5

21 years and over 62 years and over 65 years and over Male Female

748,445 175,111 152,402 60,002 92,400

71.4 16.7 14.5 5.7 8.8

Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

317

Table 77: Geographic Area: South Carolina Profile of Sex and Age Characteristics: 2000 Subject

Number

Percent

Total population

4,012,012

100.0

SEX AND AGE Male Female

1,948,929 2,063,083

48.6 51.4

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years

264,679 285,243 290,479 295,377 281,714 560,831 625,124

6.6 7.1 7.2 7.4 7.0 14.0 15.6

45 to 54 years 55 to 59 years 60 to 64 years 65 to 74 years 75 to 84 years 85 years and over

550,321 206,762 166,149 270,048 165,016 50,269

13.7 5.2 4.1 6.7 4.1 1.3

35.4

(X)

18 years and over Male Female

3,002,371 1,432,413 1,569,958

74.8 35.7 39.1

21 years and over 62 years and over 65 years and over Male Female

2,814,131 581,573 485,333 196,734 288,599

70.1 14.5 12.1 4.9 7.2

Median age (years)

Legend: (X) Not Applicable Source: U.S. Census Bureau, Census 2000.

318

Appendix II: Lung Cancer Distribution Tables Table 41: Lung Cancer Distribution - Treatment Group versus Race Treatment Group Radiation I Chemotherapy II Surgery III Radiation + Surgery IV Radiation + Chemotherapy V Surgery + Chemotherapy VI Radiation + Surgery + Chemotherapy VII No Radiation, Surgery, and/or Chemotherapy VIII

Race White Black Other White Black Other White Black Other White Black Other White Black Other White Black Other White Black Other White Black Other

Frequency Percent 3921 90.1 401 9.2 29 0.7 6026 93.1 383 5.9 63 1.0 11967 94.0 659 5.2 102 0.8 990 93.1 64 6.0 9 0.9 7262 91.3 627 7.9 66 0.8 1166 93.4 75 6.0 8 0.6 1254 93.0 81 6.0 13 1.0 8872 91.5 752 7.8 73 0.8

There were no obvious differences in treatment groups versus and distribution of race – see Table 41 in Appendix B. The majority of lung cancer cases are White ranging from 91.3% of all lung cancer cases in Group V (Radiation and Chemotherapy) to 94% of all lung cancer cases in Group III (Surgery). The classification of ―Other‖ for race

319

contained the least amount of lung cancer cases for each treatment group with each Treatment Group having a minimum of approximately one percent within each treatment classification (I – VIII).

320

Table 42: Lung Cancer Distribution - Treatment Group vs. Marital Status at Diagnosis Treatment Group Marital Status Frequency Percent Single 482 11.1 Radiation Married 2309 53.1 I Separated 46 1.1 Divorced 486 11.2 Widowed 1028 23.6 Single 652 10.1 Chemotherapy Married 3879 59.9 II Separated 63 1.0 Divorced 734 11.3 Widowed 1144 17.7 Single 1014 8.0 Surgery Married 8014 63.0 III Separated 76 0.6 Divorced 1301 10.2 Widowed 2323 18.3 Single 93 8.8 Radiation + Surgery Married 693 65.2 IV Separated 6 0.6 Divorced 108 10.2 Widowed 163 15.3 Radiation + Single 784 9.9 Chemotherapy Married 4991 62.7 V Separated 86 1.1 Divorced 982 12.3 Widowed 1112 14.0 Surgery + Single 103 8.3 Chemotherapy Married 873 69.9 VI Separated 9 0.7 Divorced 122 9.8 Radiation + Surgery + Single 98 7.3 Chemotherapy Married 934 69.3 VII Separated 11 0.8 Divorced 148 11.0 Widowed 157 11.7 321

No Radiation, Surgery, and/or Chemotherapy VIII

Single Married Separated Divorced Widowed

1201 5066 70 1039 2321

12.4 52.2 0.7 10.7 23.9

For all treatment groups in Table 42 (Treatment Group vs. Marital Status at Diagnosis), the greatest percentage of the lung cancer cases were married at the time of diagnosis ranging from 52.2 percent for Treatment Group VIII to maximum percentage of 69.9 percent for surgical and chemotherapy, Group VI.

322

Table 43: Lung Cancer Distribution - Treatment Group vs. Age Group at Diagnosis Treatment Group

Radiation I

Chemotherapy II

Surgery III

Radiation + Surgery IV

Radiation + Chemotherapy V

Surgery + Chemotherapy VI

Age Group at Diagnosis Frequency Percent > 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80 > 80 - < 90

189 573 1093 1675 821

4.3 13.2 25.1 38.5 18.9

> 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80 > 80 - < 90

404 1192 2061 2225 590

6.2 18.4 31.8 34.4 9.1

> 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80 > 80 - < 90 > 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80 > 80 - < 90 > 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80

442 1662 3893 5366 1365 59 178 358 395 73 666 1743 2705 2361

3.5 13.1 30.6 42.2 10.7 5.6 16.8 33.7 37.2 6.9 8.4 21.9 34.0 29.7

> 80 - < 90 > 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80

480 92 287 479 350

6.0 7.4 23.0 38.4 28.0

323

Radiation + Surgery + Chemotherapy VII

No Radiation, Surgery, and/or Chemotherapy VIII

> 80 - < 90 > 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80 > 80 - < 90 > 40 - < 50 > 50 - < 60 > 60 - < 70 > 70 - < 80 > 80 - < 90

41 133 345 485 366 19 367 1199 2462 3666 2003

3.3 9.9 25.6 36.0 27.2 1.4 3.8 12.4 25.4 37.8 20.7

The greatest percentage for age group IV (> 70 - < 80) were found in Treatment Groups I (38.5), II (34.4), III (42.2), IV (37.2), V (29.7), and VIII (37.8). The remaining two treatment groups had the highest percentage in the third age group, > 60 - < 70, Treatment Group VI (38.4) and Treatment Group VII (36.0).

324

Appendix III: Chemotherapy Agents Table 78: Chemotherapy Agents for Lung Cancer

Source: Alexander Spira, M.D., Ph.D., and David S. Ettinger, M.D.; N Engl J Med 2004; 350:379-92.

325

Appendix IV: Calculation of the Overall Interaction Effect Calculation of the overall effect for the treatment type received (variables extracted for the Multinomial Logistic Regression Model).

Stage The interaction terms containing stage with the main effects are included in the following equation that was originally extracted from the full model. Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI The terms that contain stage are identified and include the main effect: Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI The following equation results: Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 8 stageI*gradeI + 9 stageI* age_groupI These terms must be evaluated separately to assess the effect of stage on the outcome. In other words, as there are three interaction terms with stage, three separate equations containing stage are calculated for gender, grade and age group. In Part I below, the example treatment is radiation, stageI (stageI = stage 1 coded as 1 and stage IV coded as 0 (reference). Part II will examine stage and grade and Part III will assess stage and age group. Part I: Evaluating stage and gender Stage I: Logit (Y= Radiation | stageI = 1, genderI) =  + 1 genderI + 2 + 4 gradeI + 5age_groupI + 6 genderI* + 8 *gradeI + 9 * age_groupI

326

Stage IV: Logit (Y= Radiation | stageI = 0, genderI) =  + 1 genderI + 4 gradeI + 5age_groupI Subtracting stage I from stage IV, the following is given: Logit (Y= Radiation | stageI = 1, genderI) = 2 + 6 genderI* + 8 *gradeI + 9 * age_groupI Looking at females as compared to males with gender = 1 for females and gender = 0 for males. Logit (Y= Radiation | stageI = 1, genderI = 1) = 2 + 6 + 8 *gradeI + 9 * age_groupI Logit (Y= Radiation | stageI = 1, genderI = 0) = 2 + 8 *gradeI + 9 * age_groupI

The Odds Ratio for females with stage 1 lung cancer (grade and age group are fixed or controlled for) is given as: OR = exp (2 + 6)

Part II: Evaluating stage and grade Stage I: Logit (Y= Radiation | stageI = 1, genderI) =  + 1 genderI + 2 + 4 gradeI + 5age_groupI + 6 genderI* + 8 *gradeI + 9 * age_groupI Stage IV: Logit (Y= Radiation | stageI = 0, genderI) =  + 1 genderI + 4 gradeI + 5age_groupI Subtracting stage I from stage IV, the following is given: Logit (Y= Radiation | stageI = 1, genderI) = 2 + 6 genderI* + 8 *gradeI + 9 * age_groupI Looking at grade I as compared to grade IV with gradeI = 1 for grade I and gradeI = 0 for grade IV. Logit (Y= Radiation | stageI = 1, gradeI = 1) = 2 + 6 genderI* + 8 + 9 * age_groupI Logit (Y= Radiation | stageI = 1, gradeI = 0) = 2 + 6 genderI* + 9 * age_groupI The Odds Ratio for stage 1 grade 1 lung cancer (gender and age group are fixed or

327

controlled for) is given as: OR = exp (2 + ) Part III: Evaluating stage and age group Stage I: Logit (Y= Radiation | stageI = 1, age_groupI) =  + 1 genderI + 2 + 4 gradeI + 5age_groupI + 6 genderI* + 8 *gradeI + 9 * age_groupI Stage IV: Logit (Y= Radiation | stageI = 0, age_groupI =  + 1 genderI + 4 gradeI + 5age_groupI Subtracting stage I from stage IV, the following is given: Logit (Y= Radiation | stageI = 1, age_groupI) = 2 + 6 genderI* + 8 *gradeI + 9 * age_groupI Looking at grade I as compared to grade IV with gradeI = 1 for grade I and gradeI = 0 for grade IV. Logit (Y= Radiation | stageI = 1, age_groupI = 1) = 2 + 6 genderI* + 8 *gradeI + 9 Logit (Y= Radiation | stageI = 1, age_groupI = 0) = 2 + 6 genderI* + 8 *gradeI + The Odds Ratio for stage 1 in age group I (gender and grade are fixed or controlled for) is given as: OR = exp (2 + )

Grade Given the equation extracted from the full model: Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI The terms that contain grade are identified and include the main effect: Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI The following equation results: 328

Logit (Y= Treatment | X) =  + 2 stageI + 4 gradeI + 8 stageI*gradeI Next, the effect of grade on the probability of receiving radiation therapy as a treatment, given that the patient is at stageI, is determined as: Grade I: Logit (Y=Radiation|grade=1, stageI) =  + 2 stageI + 4 + 8 *stageI Grade IV: Logit (Y=Radiation|grade=0, stageI) =  + 2 stageI By subtracting the Logit for grade IV from Logit for grade I, the following equation is given as: Logit (Y=Radiation |grade=1, stageI) = 4 + 8 *stageI At the variable stageI which is coded as 1 for stageI and 0 for stageI (V = reference), the results are given as: Logit (Y=Radiation |grade=1, stageI =1) = 4 Logit (Y=Radiation |grade=1, stageI =0) = 4

+ 8

Estimating the overall effect of grade (grade I as compared to grade IV) on the probability of receiving radiation treatment, after adjusting for stageI (stage=1) results in the following equation for the Odds Ratio is given as: OR = exp (4 + 8).

Marital Status The interaction terms containing marital status with the main effects are included in the following equation that was originally extracted from the full model. Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI The terms that contain stage are identified and include the main effect: Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI +  3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI 329

The following equation results: Logit (Y= Treatment | X) =  + 1 genderI + 3 marital_statusI + 7 genderI* marital_statusI Evaluating marital_statusI for marital status = I (single) and marital_statusI for marital status = V (widowed), the following is given: Marital Status I: Logit (Y= Radiation | marital_statusI = 1, genderI) =  + 1 genderI + 3 + 7 genderI* Marital Status V: Logit (Y= Radiation | marital_statusI = 0, genderI) =  + 1 genderI

Subtracting marital status I from marital status IV, the following equation results: Logit (Y= Radiation | marital_statusI = 1, genderI) = 3 + 7 genderI* Looking at females as compared to males with genderI = 1 for females and gradeI = 0 for males. Logit (Y= Radiation | marital_statusI = 1, genderI = 1) = 3 + 7 Logit (Y= Radiation | marital_statusI = 1, genderI = 0) = 3

The Odds Ratio for the overall interaction effect given marital status for females as compared to males is given as: OR = exp (3 + 7)

Age Group The interaction terms containing age group with the main effects are included in the following equation that was originally extracted from the full model. Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI + 10 morphologyI + 11 raceI The terms that contain age group are identified and include the main effect: Logit (Y= Treatment | X) =  + 1 genderI + 2 stageI + 3 marital_statusI + 4 gradeI + 330

5age_groupI + 6 genderI*stageI + 7 genderI* marital_statusI + 8 stageI*gradeI + 9 stageI* age_groupI The following equation results: Logit (Y= Treatment | X) =  + 2 stageI + 5age_groupI + 9 stageI* age_groupI For the next example, the treatment group still remains as Radiation alone. Evaluating age_groupI for age group I = 1 (> 40 - < 50 year) and age_groupI for age group V = 0 (> 80 - < 90 years), the following is given: Marital Status I: Logit (Y= Radiation | age_groupI = 1, stageI) =  + 2 stageI + 5 + 9 stageI* Marital Status V: Logit (Y= Radiation | age_groupI = 0, stageI) =  + 2 stageI

Subtracting age group I from age group V, the following equation results: Logit (Y= Radiation | age_groupI = 1, stageI) = 5 + 9 stageI* Looking at stage I and stage IV with stageI = 1 for stage I and stageI = 0 for stage IV. Logit (Y= Radiation | marital_statusI = 1, stageI = 1) = 5 + 9 Logit (Y= Radiation | marital_statusI = 1, stageI = 0) = 5

The Odds Ratio for the overall interaction effect given age group I controlling for stage is given as: OR = exp (5 + 9) Logit (Y= Radiation | stageI = 1, grade =1) =  + 1 gender + 2 stageI + 4 gradeI + 5age_groupI + 6 gender*stageI + 8 stageI*gradeI + 9 stage* age_groupI Logit (Y= Radiation | stageI = 1, age_group =1) =  + 1 gender + 2 stageI + 4 gradeI + 5age_groupI + 6 gender*stageI + 8 stageI*gradeI + 9 stage* age_groupI

331

ABOUT THE AUTHOR My story starts out when I was thirteen (13) years old. My father died of cancer. The unintentional consequences of my career path has lead me on a course through cancer diagnosis and therapy to where I am today, redesigning my career to include Cancer Care research. My education, experiences and professional journey began, after high school, with technical training in Radiology and Nuclear Medicine which are modalities used in the diagnosis of Cancer. I continued on growing professionally with a Masters Degree in Medical Physics, the knowledge I gained allowed me to enter into cancer treatment through Radiation Therapy Industry. Along the way, in 1972 I married my husband, Stan and in 1985 we had a son, J Stephen. I am very grateful for their help and support during my life, especially their encouragement as I reshape my career once again. Without their confidence, love and caring I could not have made this rewarding personal progress, I thank them. My PhD from the Department of Epidemiology and Biostatistics will make me uniquely qualified to research issues that include my education and experience in Radiological Physics. I find Radiation Epidemiology is receiving great respect and becoming a specialty area that I can assimilate easily. Finally, I do not know how to adequately thank, Dr. Mason, my Mentor, Advisor, Co-Major Professor, and Friend. I would also like to thank my Co-Major Professor, Dr. Stockwell, and other committee members, Dr. Dagne and Dr. Zhukov for all their hard work, direction, guidance and sustained confidence throughout my research to complete my PhD.

332

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Gender Differences in Lung Cancer Treatment - Scholar Commons

University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 2011 Gender Differences in Lung Cancer Treatment and...

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