Heterogeneous survivor average causal effects beyond monotonicity: Applications to a clinical trial evaluating mechanical ventilation strategies
超越单调性的异质幸存者平均因果效应：机械通气临床试验应用AI 解读 ↓
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超越单调性的异质幸存者平均因果效应：机械通气临床试验应用
- 研究问题
- 当结果因死亡而截断、治疗可能同时改善或损害生存时，如何不依赖单调性分析始终幸存者的条件处理效应，并识别异质性？
- 方法与思路
- 作者用刻画潜在主分层归属的敏感性参数建立框架，采用 BART 贝叶斯估计，并构造变量重要性指标与利用后验分布的条件推断树。
- 材料陈述的贡献
- 摘要称方法改善非线性异质性下的个体效应估计与变量重要性恢复，并在 PEEP 试验中发现平均结果所掩盖的获益及受损亚组。
- 阅读关注点
- 应核对敏感性参数与识别条件的关系、始终幸存者的潜在归属、先验及 BART 设定，以及亚组结论对敏感性参数和后验不确定性的依赖。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出全部识别条件，不依赖单调性仍涉及敏感性设定，亚组结论不能直接泛化。
原文摘要
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.