Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare

Yingchao Zhong, Chang Wang and Lu Wang
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Yingchao Zhong: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
Chang Wang: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
Lu Wang: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA

Stats, 2021, vol. 4, issue 4, 1-17

Abstract: In this paper, we consider personalized treatment decision strategies in the management of chronic diseases, such as chronic kidney disease, which typically consists of sequential and adaptive treatment decision making. We investigate a two-stage treatment setting with a survival outcome that could be right censored. This can be formulated through a dynamic treatment regime (DTR) framework, where the goal is to tailor treatment to each individual based on their own medical history in order to maximize a desirable health outcome. We develop a new method, Survival Augmented Patient Preference incorporated reinforcement Q-Learning (SAPP-Q-Learning) to decide between quality of life and survival restricted at maximal follow-up. Our method incorporates the latent patient preference into a weighted utility function that balances between quality of life and survival time, in a Q-learning model framework. We further propose a corresponding m-out-of-n Bootstrap procedure to accurately make statistical inferences and construct confidence intervals on the effects of tailoring variables, whose values can guide personalized treatment strategies.

Keywords: dynamic treatment regime; m-out-of-n bootstrap; personalized treatment decision; Q-learning; right censored survival (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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