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.

翻译：受急性呼吸窘迫综合征网络（ARDSNetwork）ARDS呼吸管理（ARMA）试验的启发，我们开发了一种灵活的贝叶斯机器学习方法，以在临床结局受截断影响时，估计始终幸存者层中的平均因果效应和异质性因果效应。我们采用贝叶斯加性回归树（BART）灵活地为潜在结局和潜在层成员关系指定独立模型。在对ARMA试验的分析中，我们发现，对于急性肺损伤患者，低潮气量治疗在“回家时间”这一结局上总体有益，但在始终幸存者中，治疗效果存在显著异质性，这种异质性主要由性别和基线肺泡-动脉氧梯度（一种肺功能的生理指标和低氧血症来源）驱动。这些发现展示了所提出的方法如何指导该领域未来试验的预后富集。我们还通过模拟研究表明，我们提出的贝叶斯机器学习方法在减少始终幸存者平均因果效应和异质性因果效应的估计偏差方面，优于其他参数方法。