Am J Epidemiol. 2023 Jun 2;192(6):1006-1015. doi: 10.1093/aje/kwad038.
Many studies encounter clustering due to multicenter enrollment and nonmortality outcomes, such as quality of life, that are truncated due to death-that is, missing not at random and nonignorable. Traditional missing-data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) carried out among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e., the survivor average causal effect (SACE)). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete-case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and software code to support future research.
许多研究由于多中心招募和非致死性结局（如生活质量）而存在聚类，这些结局因死亡而截断——即缺失不是随机的，且不可忽略。在存在这些综合问题的情况下，传统的缺失数据方法和目标因果估计量对于统计推断并不理想，这些问题在多中心研究和在老年人或重病患者中进行的集群随机试验（CRTs）中尤为常见。我们使用主要分层法开发了一种贝叶斯估计量，该估计量可在聚类/分层数据环境中共同识别始终存活的主要分层，并估计其中的平均治疗效果（即幸存者平均因果效应（SACE））。在模拟中，我们观察到我们的方法具有低偏差和良好的覆盖率。在一个有启发性的 CRT 中，SACE 和完全案例分析的估计值在大小上有所不同，但两者都很小，且均与零效应不矛盾。然而，SACE 估计值具有明确的因果解释。在具有信息截断和聚类的研究中评估严格定义的 SACE 估计量的选项可以为研究参与者的一个重要子集提供更多的见解。基于模拟研究和 CRT 重新分析，我们为 CRT 中使用 SACE 提供了实用建议，并提供了支持未来研究的软件代码。
