Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models.

ABSTRACT: Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms

SUBMITTER: Henderson NC 

PROVIDER: S-EPMC8972560 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature