AI 中文总结
针对重症监护临床试验中因死亡截断的结果，提出无单调性框架识别条件幸存者平均因果效应，基于BART的贝叶斯方法估计个体效应，应用于ARDS PEEP试验揭示被总体结果掩盖的异质性亚组。
AI 中文摘要
重症监护中的临床试验通常评估因死亡而截断的结果，例如存活出院时间，对于在一种或两种治疗策略下无法存活的患者，治疗效果并未得到明确定义。主分层为定义幸存者因果效应提供了自然框架，但现有方法通常依赖于单调性假设，在治疗可能以相反方向影响生存的情况下，这些假设可能不成立。这一担忧在ARDS Network试验中得到了体现，该试验比较了较低与较高呼气末正压（PEEP），其中较高的PEEP可能通过改善肺泡复张使某些患者受益，而通过过度膨胀或血流动力学损害伤害其他患者。此外，原始试验基本无效的平均结果并不排除存在可能受益于或受到较高PEEP伤害的临床意义亚组的可能性。我们提出了一个无单调性框架，用于识别条件幸存者平均因果效应（CSACE），使用一个可解释的敏感性参数来表征潜在的主分层成员身份。然后，我们开发了一种基于BART的灵活贝叶斯估计策略，包括基于后验均值的变量重要性度量，以及利用个体化CSACE的完整后验抽样的分布感知条件推理树。在模拟研究中，与线性建模相比，所提出的基于BART的方法改善了个体化CSACE估计和变量重要性恢复，尤其是在非线性治疗效应异质性下。应用于ARDS PEEP试验时，我们的方法揭示了估计的始终幸存者中临床可解释的异质性，识别出具有后验证据表明受益或受伤害的亚组，而这些在总体无效的试验结果中被掩盖。
英文摘要
Clinical trials in critical care often evaluate outcomes that are truncated by death, such as time to discharge alive, for which treatment effects are not well defined among patients who would not survive under one or both treatment strategies. Principal stratification provides a natural framework for defining survivor causal effects, but existing approaches often rely on monotonicity assumptions that may be implausible in settings where treatment can affect survival in competing directions. This concern is illustrated by the ARDS Network trial of lower versus higher positive end-expiratory pressure (PEEP), in which higher PEEP may benefit some patients by improving alveolar recruitment while harming others through over-distention or hemodynamic compromise. Moreover, the largely null average findings of the original trial do not rule out the possibility of clinically meaningful subgroups that may benefit from or be harmed by higher PEEP. We propose a monotonicity-free framework for identifying conditional survivor average causal effects (CSACE) using an interpretable sensitivity parameter that characterizes latent principal-stratum membership. We then develop a flexible Bayesian estimation strategy based on BART, a posterior-mean-based variable importance measure, and a distribution-aware conditional inference tree that uses the full posterior draws of individualized CSACEs. In simulation studies, the proposed BART-based approach improves individualized CSACE estimation and variable-importance recovery compared with linear modeling, especially under nonlinear treatment-effect heterogeneity. Applied to the ARDS PEEP trial, our method reveals clinically interpretable heterogeneity among estimated always-survivors, identifying subgroups with posterior evidence of benefit or harm that are obscured by the overall null trial results.
