  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 models for the potential outcomes and
latent strata 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 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. We also demonstrated through a simulation study
that our proposed Bayesian machine learning approach outperforms other
parametric methods in reducing the estimation bias in both the average causal
effect and heterogeneous causal effects for always-survivors.
