Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects (HTE) based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of HTE. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation (MMI) and Bayesian MMI have better performance than other available methods, and that Bayesian MMI has lower bias and closer to nominal coverage than standard MMI when there are model specification or compatibility issues.

翻译：理解治疗效应在不同亚组间是否存在及如何变化对指导临床实践与建议至关重要。因此，基于预先指定的潜在效应修饰符评估异质性治疗效应（HTE）已成为现代随机试验中的常见目标。然而，当一个或多个潜在效应修饰符缺失时，完整病例分析可能导致偏倚和覆盖率不足。尽管针对个体随机试验中效应修饰符数据缺失的情况，已有多种处理缺失数据的统计方法被提出和比较，但在集群随机设置中仍缺乏相关指南。在集群随机环境下，效应修饰符、结局甚至缺失机制中的群内相关性可能进一步威胁HTE的准确评估。本文通过一项持续结局且二元效应修饰符数据缺失的集群随机试验模拟研究，比较了多种缺失数据处理方法的性能，并利用工作、家庭与健康研究的真实数据进一步说明。结果表明，多水平多重插补（MMI）和贝叶斯MMI表现优于其他可用方法，且在存在模型设定或兼容性问题时，贝叶斯MMI比标准MMI具有更低的偏倚和更接近名义覆盖率的表现。