Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring

Hunyong Cho, Shannon T Holloway, David J Couper and Michael R Kosorok

Biometrika, 2023, vol. 110, issue 2, 395-410

Abstract: SummaryWe propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomial rates of convergence. Simulations and analysis of the Atherosclerosis Risk in Communities study data suggest that the new estimator brings higher expected outcomes than existing methods in various settings.

Keywords: Conditionally independent censoring; Dynamic treatment regime; Empirical process; Precision medicine; Random forest; Reinforcement learning; Survival analysis (search for similar items in EconPapers)
Date: 2023
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