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.

翻译：在群组随机交叉（CRXO）试验中，个体群组被随机分配至两种或多种交替治疗序列。由于各群组作为自身对照，CRXO设计通常比常规平行组设计具有更高的统计效率。在可用群组数量有限的健康研究领域，CRXO试验正日益普及。此外，在危重患者试验中，研究者常需评估治疗对次要非终点结局的影响，但这类研究中常存在患者未能存活至完整记录这些测量指标的情况。本文提出一个因果推断框架及治疗效果估计方法，以解决CRXO试验中因死亡导致的截断问题。我们以幸存者平均因果效应（SACE）作为目标估计量——这是一种通过主分层法获得的定义明确的亚组治疗效果。我们提出了新颖的结构性和标准建模假设，使得在贝叶斯范式下估计SACE成为可能。通过模拟研究，我们评估了所提贝叶斯方法在CRXO试验场景中估计SACE的小样本性能。我们将该方法应用于一项先前开展的两周期横断面CRXO研究，该研究比较了质子泵抑制剂与组胺-2受体阻滞剂对需要侵入性机械通气的成人住院时长的影响。