Faculty Collaboration Database

Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes. Lifetime Data Anal 2024 Jan;30(1):181-212

Date

09/03/2023

Pubmed ID

37659991

Pubmed Central ID

PMC10764602

DOI

10.1007/s10985-023-09605-8

Scopus ID

2-s2.0-85169623013 (requires institutional sign-in at Scopus site)

Abstract

To achieve the goal of providing the best possible care to each individual under their care, physicians need to customize treatments for individuals with the same health state, 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.

Author List

Li X, Logan BR, Hossain SMF, Moodie EEM

Author

Brent R. Logan PhD Director, Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Bayes Theorem
Computer Simulation
Decision Making
Hematopoietic Stem Cells
Humans
Precision Medicine
Survival Analysis
Transplantation, Homologous