# Individualized Treatment Effects with Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Models

## TL;DR

This paper introduces a flexible non-parametric Bayesian model using accelerated failure time models and Bayesian additive regression trees to analyze heterogeneous treatment effects in time-to-event data, with minimal tuning.

## Contribution

It presents a novel non-parametric Bayesian approach for estimating individualized treatment effects in censored survival data, requiring little user input and accommodating complex heterogeneity.

## Key findings

- Detected significant heterogeneity in treatment effects across patients.
- High proportions of patients showed treatment effects differing from the average.
- Method effectively analyzes large clinical trial data for personalized medicine.

## 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 non-parametric 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 non-parametric 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 of tuning parameter selection or subgroup specification. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect.