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

• 成果类型: Article; Early Access
• 署名作者: Cho, Hunyong; Holloway, Shannon T.; Couper, David J.; Kosorok, Michael R.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asac047
• 发表日期: 2022
• 关键词: inference; safety; RISK

摘要

We 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.