Censoring-adjusted tree-based policy learning for estimating dynamic treatment regimes with censored outcomes

Dynamic Treatment Regimes (DTRs) provide a systematic approach for making sequential treatment decisions that adapt to individual patient characteristics, particularly in clinical contexts where survival outcomes are of interest. We explore ways to learn effective DTRs, from observational data, using Censoring-Adjusted Tree-based Reinforcement Learning (CA-TReL) - a novel framework to address the complexities associated with censored data when estimating optimal DTRs. By enhancing traditional tree-based reinforcement learning methods with augmented inverse probability weighting (AIPW) and censoring-adjusted estimation, CA-TReL delivers robust and interpretable treatment strategies. Through extensive simulation studies and real-world validation using the SANAD epilepsy dataset, CA-TReL achieved statistically better performance relative to several state-of-the-art methods - including OWL, RWL, TBWL, ASCL, and DWsurv - across key metrics such as restricted mean survival time (RMST) and decision-making accuracy. This work represents a step forward in advancing personalized and data-driven treatment strategies across diverse healthcare settings.