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

TL;DR

This paper introduces a novel method for estimating optimal dynamic treatment regimes in indefinite-horizon survival analysis, accommodating varying treatment stages and leveraging generalized survival random forests for improved survival time estimation.

Contribution

It presents a new algorithm that handles indefinite treatment stages, uses data pooling to prevent dimensionality issues, and provides theoretical guarantees for the estimator's consistency.

Findings

The estimator is consistent and theoretically sound.

Simulation studies show good finite-sample performance.

Application to insurance data demonstrates practical utility.

Abstract

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.