July 10, 2023 Webinar
A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial
Fan Li, PhD
Abstract
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.
Short Bio
Dr. Fan Li is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health, and faculty member in the Center for Methods in Implementation and Prevention Science. He received his Ph.D. in biostatistics from Duke University in May 2019, and joined the Yale Biostatistics faculty in July 2019. His main expertise is in the development of methods for designing and analyzing pragmatic cluster randomized trials, causal inference for randomized trials and observational studies, and techniques for improving internal and external validity for treatment comparison under different study designs. He is the Principal Investigator of a Patient-Centered Outcomes Research Institute (PCORI)-funded methods award that investigates new study planning methods and software for testing treatment effect heterogeneity in cluster randomized trials. His research has also been supported by several additional PCORI-funded and NIH-funded awards.