Dynamic Treatment Regimes using Bayesian Additive Regression Trees for Censored Outcomes

TL;DR

This paper introduces a Bayesian additive regression trees approach for estimating dynamic treatment regimes with censored survival outcomes, improving personalized treatment strategies in diseases like cancer.

Contribution

It adapts Bayesian additive regression trees to censored outcomes within the DTR framework, providing a flexible and extendable method compared to traditional Q-learning.

Findings

The proposed BART-based method outperforms Q-learning in simulations.

The approach effectively handles censored survival data.

An R package implementation facilitates practical application.

Abstract

To achieve the goal of providing the best possible care to each patient, physicians need to customize treatments for patients with the same diagnosis, especially when treating diseases that can progress further and require additional treatments, such as cancer. Making decisions at multiple stages as a disease progresses can be formalized as a dynamic treatment regime (DTR). Most of the existing optimization approaches for estimating dynamic treatment regimes including the popular method of Q-learning were developed in a frequentist context. Recently, a general Bayesian machine learning framework that facilitates using Bayesian regression modeling to optimize DTRs has been proposed. In this article, we adapt this approach to censored outcomes using Bayesian additive regression trees (BART) for each stage under the accelerated failure time modeling framework, along with simulation studies and a real data example that compare the proposed approach with Q-learning. We also develop an R wrapper function that utilizes a standard BART survival model to optimize DTRs for censored outcomes. The wrapper function can easily be extended to accommodate any type of Bayesian machine learning model.