A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects - Applications to a Critical Care Trial

Aug 5, 2026

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Assessing treatment effect heterogeneity is central to causal inference and personalized clinical decision-making, yet it is difficult when patient-centered outcomes are truncated by terminal events such as death. This work proposes a flexible Bayesian machine learning framework to estimate average and heterogeneous causal effects among always-survivors using principal stratification. The method adopts Bayesian additive regression trees to model potential outcomes and latent stratum membership, and is applied to an ARDSNet ARMA critical care trial, revealing overall benefit of low tidal volume while showing strong heterogeneity driven by baseline sex and the alveolar-arterial oxygen gradient.

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CategoryResearch & Report

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Pages24

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Introduction

Treatment effect heterogeneity and clinical motivation

Truncation by death and limitations of direct survivors-only analysis

Motivating ARDSNet ARMA trial

arXiv :2204 .06657v3 [ stat .AP] 20 Jun 2023 A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL BY XINYUAN CHEN1,* , MICHAEL O. HARHAY2,†, GUANGYU TONG3,4,‡ AND FAN LI3,4,§ 1 Department of Mathematics and Statistics, Mississippi State University,*[xchen@math.msstate.edu](xchen@math.msstate.edu) 2 Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, †[mharhay@pennmedicine.upenn.edu](mharhay@pennmedicine.upenn.edu) 3 Department of Biostatistics, Yale University School of Public Health,‡[guangyu.tong@yale.edu](guangyu.tong@yale.edu);§[fan.f.li@yale.edu](fan.f.li@yale.edu) 4 Center for Methods in Implementation and Prevention Science, Yale University School of Public Health, ‡[guangyu.tong@yale.edu](guangyu.tong@yale.edu);§[fan.f.li@yale.edu](fan.f.li@yale.edu) Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants, in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home, but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and source of hypoxemia) . These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field. 1. Introduction. Personalized medicine, whereby healthcare is tailored for each individual patient, is the pursuit of contemporary clinical research and practice. For healthcare practitioners and clinicians, achieving this goal hinges upon the successful detection and an understanding of the heterogeneity in participants’ response to treatment strategies based on their individual characteristics. Capturing factors prognostic of a stronger or weaker response to a trial intervention is especially important in critical care, where conditions such as cardiogenic shock, sepsis, and acute respiratory failure are defined by syndromic criteria such that individuals with the same condition can vary in their biologic and clinical presentation, and thus optimal treatment strategies can vary among clinical populations. While examination of treatment effect heterogeneity for short-term mortality is difficult due to the small sample sizes common in critical care trials (Harhay et al., 2014), this outcome is at least available for all individuals, and recent innovations in statistical learning Keywords and phrases: acute lung injury, Bayesian additive regression trees, causal inference, heterogeneity of treatment effects, principal stratification, truncation by death. 2 increasingly permit such examinati

FAQs

Why is estimating treatment effect heterogeneity challenging when outcomes are truncated by death?

Because non-mortality outcomes are unobservable, undefined, or ambiguously measured for participants who do not survive, and this post-randomization truncation can be informative and induce selection bias.

What population does the proposed method focus on?

It targets the always-survivors stratum, where causal conclusions can be drawn despite truncation of patient-centered outcomes by death.

Which model components does the Bayesian approach use in this framework?

It uses Bayesian additive regression trees to flexibly specify mean models for potential outcomes and to model latent stratum membership.

Aug 5, 2026

Upload by Himbo