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Activity Number: 118 - Emerging Challenges in Precision Medicine
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304787 Presentation
Title: Characterizing Outcome Distributions of Dynamic Treatment Regimes
Author(s): Daniel Lizotte*
Companies: The University of Western Ontario
Keywords: dynamic treatment regimes; reinforcement learning; prediction intervals; tolerance intervals; multiobjective optimization; sequential decision-making

The goal of this work is to better convey the evidence for or against clinically significant differences in patient outcomes induced by different dynamic treatment regimes by examining not only differences in mean outcomes, but differences in outcome distributions. We present a framework for computing and presenting prediction regions and tolerance regions for the outcomes of a dynamic treatment regime operating within a multi-objective Markov decision process (MOMDP). Our framework draws on two bodies of existing work: one in computer science for learning in MOMDPs, and one in statistics for uncertainty quantification. We review the relevant methods from each body of work, present our framework, and illustrate its use in a precision medicine problem. Finally, we discuss potential future directions of this work for supporting sequential decision-making.

Authors who are presenting talks have a * after their name.

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