Partially Observable Markov Decision Processes for Prostate Cancer Screening

Prostate cancer is the most common solid tumor that affects American men. Screening is carried out using prostate-specific antigen (PSA) tests and biopsies. This dissertation investigates the optimal design of screening policies that tradeoff the cost and harm to patients of screening with the benefits of early detection. We report on partially observable Markov decision processes (POMDPs) for the study of prostate cancer screening decisions. A Markov process represents the occurrence and progression of prostate cancer in our models. The core states are the patients' prostate cancer related health states. PSA test results and biopsy results are the observations.;First, a POMDP model is proposed for prostate biopsy referral decisions assuming the patient undergoes annual PSA screening. The objective is to maximize expected quality adjusted life years (QALYs). Several structural properties which give insights into the optimal biopsy referral policy over the course of a patient's lifetime are proved. An age-specific prostate biopsy referral policy is obtained and sensitivity analysis is used to show how the optimal policy and value are sensitive to parameters in the model.;Next, a POMDP model is proposed for optimizing both PSA screening and biopsy referral decisions. We use this model to compute the optimal policy for PSA testing or biopsy at each decision epoch over the course of a patient's lifetime. The objective of the model is to maximize the difference between rewards for QALYs and the cost of screening, biopsy, and treatment. The optimal policy is compared to no screening and the traditional guideline from the published literature. Benefits of screening are shown in terms of the expected QALYs and costs, and sensitivity analysis is performed with respect to cost parameters.;Finally, a multi-stage POMDP is proposed to consider the coordination of prostate cancer screening and treatment decisions. Multiple treatment options, including active surveillance and radical prostatectomy, are considered in the model. The model is extended to include additional actions, core states, and observations at each decision epoch. A new sampling-based approximation method is developed to solve the extended POMDP model. Structural properties of the model are discussed and a method to take advantage of the underlying structure is incorporated into the approximation method. Computational experiments are presented which compare the new approximation method to other previously proposed methods to show the effectiveness and efficiency of our proposed approximation method. Empirical results for the optimal screening and treatment policy are presented. Sensitivity analysis is used to show how the availability of active surveillance (AS) influences the optimal screening policy and the expected QALYs.