A Bayesian approach to the survivor average causal effect in cluster-randomized crossover trials
集群随机交叉试验中存活者平均因果效应的贝叶斯方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以死亡截断、主层分层及存活者平均因果效应为核心，直接对应因果推断。
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集群随机交叉试验中存活者平均因果效应的贝叶斯方法
- 研究问题
- 在集群随机交叉试验中，死亡导致次要非终末结果无法观测时，如何定义并估计具有明确因果含义的存活者处理效应？
- 方法与思路
- 作者通过主层分层定义存活者平均因果效应，提出支持估计的结构及建模假设，并在贝叶斯框架中处理死亡截断。
- 材料陈述的贡献
- 摘要称建立适用于此类试验的因果推断与估计框架，以模拟考察小样本表现，并用于机械通气患者用药与住院时长研究。
- 阅读关注点
- 应核对存活主层的定义、识别所需结构假设、集群和时期依赖的建模，以及先验选择和死亡机制假设的敏感性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列明识别假设、先验及应用结果，不能判断存活者效应的识别强度。
原文摘要
In cluster-randomized crossover (CRXO) trials, groups of individuals are randomly assigned to two or more sequences of alternating treatments. Since clusters serve as their own control, the CRXO design is typically more statistically efficient than the usual parallel-arm design. CRXO trials are increasingly popular in many areas of health research where the number of available clusters is limited. Further, in trials among severely ill patients, researchers often want to assess the effect of treatments on secondary non-terminal outcomes, but frequently in these studies, there are patients who do not survive to have these measurements fully recorded. In this paper, we provide a causal inference framework and treatment effect estimation methods for addressing truncation by death in the setting of CRXO trials. We target the survivor average causal effect (SACE) estimand, a well-defined subgroup treatment effect obtained via principal stratification. We propose novel structural and standard modeling assumptions that enable estimating the SACE within a Bayesian paradigm. We evaluate the small-sample performance of our proposed Bayesian approach for estimation of the SACE in CRXO trial settings via simulation studies. We apply our methods to a previously conducted two-period cross-sectional CRXO study examining the impact of proton pump inhibitors compared to histamine-2 receptor blockers on length of hospitalization among adults requiring invasive mechanical ventilation.