An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes

We propose a new method in indefinite-horizon settings for estimating optimal dynamic treatment regimes for time-to-event outcomes. This method allows patients to have different numbers of treatment stages and is constructed using generalized survival random forests to maximize mean survival time. We use summarized history and data pooling, preventing data from growing in dimension as a patient's decision points increase. The algorithm operates through model re-fitting, resulting in a single model optimized for all patients and all stages. We derive theoretical properties of the estimator such as consistency of the estimator and value function and characterize the number of refitting iterations needed. We also conduct a simulation study of patients with a flexible number of treatment stages to examine finite-sample performance of the estimator. Finally, we illustrate use of the algorithm using administrative insurance claims data for pediatric Crohn's disease patients.