Stochastic Models for Improving Screening and Surveillance Decisions for Prostate Cancer Care by Christine Barnett A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Industrial and Operations Engineering) in The University of Michigan 2017 Doctoral Committee: Professor Brian T. Denton, Chair Associate Professor Mariel S. Lavieri Professor Lawrence M. Seiford Assistant Professor Scott A. Tomlins Christine L. Barnett
[email protected] ORCID iD: 0000-0002-1465-7623 c Christine L. Barnett 2017 DEDICATION For Jon, Amy, and Kate. ii ACKNOWLEDGEMENTS First, I would like to thank my advisor, Dr. Brian Denton, for his guidance and support over the past five years. I am grateful to have had him as a mentor through this experience, and feel prepared for the next stage in my career thanks to his encouragement and guidance. This work was supported in part by the National Sci- ence Foundation through Grant Number CMMI 0844511 and by the National Science Foundation Graduate Research Fellowship under Grant Number DGE 1256260. I would like to thank my committee members Dr. Mariel Lavieri, Dr. Lawrence Seiford, and Dr. Scott Tomlins for serving on my committee and providing me with career advice and helpful feedback on my research. In addition, I would like to thank our collaborators from Michigan Medicine, Dr. Gregory Auffenberg, Dr. Matthew Davenport, Dr. Jeffrey Montgomery, Dr. James Montie, Dr. Todd Morgan, Dr. Scott Tomlins, and Dr. John Wei for their invaluable clinical perspective and for teaching me about the intricacies of prostate cancer screening and treatment decisions. Finally, I would like to thank my friends and family for their support throughout graduate school.