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Activity Number: 460 - Causal Methods for Discovery, Confirmation and Mechanistic Evaluation
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312472
Title: Step-Adjusted Tree-Based Reinforcement Learning (SAT-Learning) for Evaluating Nested Dynamic Treatment Regimes in Test-And-Treat Observational Studies
Author(s): Ming Tang* and Lu Wang and Jeremy Taylor and Michael Gorin
Companies: University of Michigan and University of Michigan and University of Michigan and Johns Hopkins University
Keywords: dynamic treatment regimes; multi-stage decision-making; observational data; personalized health care; test-and-treat strategy; tree-based reinforcement learning

Dynamic treatment regimes (DTRs) include a sequence of treatment decision rules, in which treatment is adapted over time in response to the changes in an individual's disease progression and health care history. In medical practice, nested test-and-treat strategies are common to improve cost-effectiveness. However, current existing statistical methods are not able to accommodate such a naturally embedded property of the treatment decision within the test decision. Therefore, we developed a new statistical learning method, Step-adjusted Tree-based Reinforcement Learning, to evaluate DTRs within such a nested multi-stage dynamic decision framework from observational data. At each step within each stage, we combined the robust semi-parametric estimation via Augmented Inverse Probability Weighting with a tree-based reinforcement learning method to deal with the counterfactual optimization. The simulation studies demonstrated robust performance of the proposed methods under different scenarios. We further applied our method to evaluate the necessity of prostate biopsy and identify the optimal test-and-treatment regimes for prostate cancer patients using active surveillance dataset

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

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