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

TL;DR

This paper introduces a reinforcement learning approach for optimizing dynamic treatment regimes in survival analysis, effectively handling dependent censoring and supporting multiple treatment stages and arms.

Contribution

It presents a novel estimator based on generalized random survival forests that maximizes survival outcomes under complex treatment and censoring scenarios.

Findings

Outperforms existing methods in simulations and data analysis

Supports flexible treatment strategies and dependent censoring

Achieves polynomial convergence rates

Abstract

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 data analysis results suggest that the new estimator brings higher expected outcomes than existing methods in various settings. An R package dtrSurv is available on CRAN.