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An Exploration of Life Expectancy Calculation Methods to Aid in Prostate Cancer Screening and Treatment Decision-Making

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An Exploration of Life Expectancy Calculation Methods to Aid in Prostate Cancer Screening and Treatment Decision-Making AN EXPLORATION OF LIFE EXPECTANCY CALCULATION METHODS TO AID IN PROSTATE CANCER SCREENING AND TREATMENT DECISION-MAKING by Dylan Jeffrey Wykes A thesis submitted to the Department of Community Health & Epidemiology In conformity with the requirements for the degree of Master of Science Queen’s University Kingston, Ontario, Canada (April, 2011) Copyright ©Dylan Jeffrey Wykes, 2011 Abstract Background: Life expectancy estimation is an important part of both screening and treatment decision-making for potentially curable prostate cancer. Clinicians’ estimation of patient life expectancy is typically made using population-based life tables and intuition and it is often inaccurate. This study explores methods to improve life expectancy prediction by formally considering patient age and co-morbid illness status, as opposed to age alone, in the development of a life expectancy prediction tool. Methods: We conducted a population-based retrospective cohort study of patients from the Ontario Cancer Registry who were curative treatment candidates, identified between 1990-1998. We analyzed data on three sub-populations of this cohort, and we used life expectancy estimates from the Ontario Life Tables. Each model utilized Cox proportional hazards analysis, and/or the declining exponential approximation of life expectancy, or both to estimate the survival experience of potential curative treatment candidates, including the impact due to both age and co-morbid illness status. We developed five separate models, tested them using a random subset of the cohort study sample, and compared their predictive accuracy by measuring both discriminative ability and calibration to determine the ‘best’ model. Further analysis was conducted to demonstrate the clinical usefulness of the ‘best’ model and to develop life expectancy reference tables. We also conducted a supplementary analysis using logistic regression to develop a model to predict the probability of 10-year survival. Results: The ‘best’ of our models, the Sub-Cohort LE prediction model, demonstrated a c-index of 0.65 and very good calibration performance. Two other models demonstrated slightly higher ii discrimination (C=0.66), but comparatively poor calibration. Further analysis revealed that our ‘best’ model violated the Cox PH assumption for age and it’s predictions consistently over- estimated observed life expectancy. Supplementary analysis of the logistic regression prediction model demonstrated a c-index of 0.70. Conclusions: Our exploration of methods to predict life expectancy resulted in modest predictive accuracy. However, based on the results of the logistic regression model, we conclude that the results of our life expectancy prediction models are reasonable, and obtaining a high level of predictive accuracy may not be possible given just age and co-morbid illness status as predictors. Further studies should continue to explore these and other methods for life expectancy prediction, being cautious of the Cox PH assumption and its implications when violated. External validation of the ‘best’ model from the current study is required before the model and its accompanying life expectancy reference tables can be recommended for use in a clinical setting for screening or treatment decision-making. iii Co-Authorship This thesis is the research work of Dylan Wykes in collaboration with his supervisors Dr. Paul Y. Peng and Dr. Patti A. Groome. iv Acknowledgements First I would like to thank my supervisors, Dr. Patti Groome and Dr. Paul Peng, for all of the time, knowledge, and patience they leant to this project. I learned an unbelievable amount from both of them, and I cannot imagine a more knowledgeable, diverse, and supportive team of supervisors. I would also like to acknowledge Dr. Groome’s other students – John, Julie, John, Coleen, Felicia and Matt for all of their suggestions and help with my project. Thanks also to everyone at the Cancer Care & Epidemiology Division of Queen’s Cancer Research Institute for their support and knowledge throughout the project, especially Sue Rohland and Jina Zhang-Salomons. I would also like to extend a thank you to Kat Cook and Lee Watkins in the Department of Community Health & Epidemiology for their help in completing this process. Finally, a big thank you to my partner, Francine, and my family for their constant support and encouragement. v Table of Contents Abstract...........................................................................................................................................ii Co-Authorship................................................................................................................................. iv Acknowledgements.......................................................................................................................... v Table of Contents ............................................................................................................................ vi List of Figures ................................................................................................................................. ix List of Tables................................................................................................................................... xi List of Equations .......................................................................................................................... xiii Chapter 1: Introduction................................................................................................................... 1 1.1 Background and Rationale .................................................................................................... 1 1.2 Study Objectives ....................................................................................................................2 1.3 Outline ................................................................................................................................... 3 1.4 References ............................................................................................................................. 4 Chapter 2: Literature Review....................................................................................................... 7 2.1 Prostate Cancer ..................................................................................................................... 7 2.2 Co-morbidity.........................................................................................................................10 2.3 Screening ..............................................................................................................................12 2.4 Treatment .............................................................................................................................13 2.5 Life Expectancy................................................................................................................... 14 2.6 Life Expectancy Calculation Tools ..................................................................................... 16 2.7 Summary ............................................................................................................................. 19 2.8 References ............................................................................................................................20 Chapter 3: Methods .................................................................................................................... 26 3.1 Overview.............................................................................................................................. 26 3.2 Study Design ....................................................................................................................... 27 3.2.1 Target Population ......................................................................................................... 27 3.2.2 Case-Cohort Population ............................................................................................... 31 3.2.3 Sub-Cohort Population ................................................................................................. 39 3.2.4 Ontario Life Tables ...................................................................................................... 41 3.3 Analysis Strategy ................................................................................................................ 42 3.3.1 Overview ..................................................................................................................... 42 vi 3.3.2 Life Expectancy Prediction Model Development ....................................................... 42 3.3.3 Comparing Accuracy of Life Expectancy Predictions ................................................ 45 3.3.4 Clinical Ssefulness of 'Most Accurate' Model ............................................................. 47 3.3.5 Life Expectancy Reference Tables .............................................................................. 47 3.3.6 Supplementary Logistic Regression Analysis ............................................................. 48 3.4 References ........................................................................................................................... 48 Chapter 4: Results........................................................................................................................ 51 4.1 Overview ............................................................................................................................
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