Date of Graduation

5-2020

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor/Mentor

Zhang, Shengfan

Committee Member/Reader

Rossetti, Manuel

Abstract

Breast cancer overdiagnosis risk is difficult to estimate and varies significantly across current research. This research establishes a simulation approach to examine the relationship between breast cancer overdiagnosis and patient outcome and understand the impact that the range of breast cancer overdiagnosis rate estimates in the current literature has on patient outcomes. Overdiagnosis is represented in this study by a set of disease regression probabilities. Using microsimulation, we evaluate patient outcome, measured by number of mammograms and lifetime breast cancer mortality risk, as a function of treatment policy and regression probability. We use numerical experiments to evaluate treatment policies and disease regression probabilities, and we conclude through sensitivity analysis that treatment policy is a statistically significant factor for patient outcome and regression probability, or overdiagnosis rate, is only partially statistically significant for patient outcome.

Keywords

Breast cancer, overdiagnosis, simulation, microsimulation

Available for download on Sunday, May 01, 2022

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