Blette Bryan S, Halpern Scott D, Li Fan, Harhay Michael O
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Stat Methods Med Res. 2024 May;33(5):909-927. doi: 10.1177/09622802241242323. Epub 2024 Apr 3.
Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects 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 heterogeneous treatment effect. 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 and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.
了解治疗效果在亚组间是否存在差异以及如何存在差异，对于指导临床实践和建议至关重要。因此，基于预先指定的潜在效应修饰因素评估异质性治疗效果已成为现代随机试验的一个共同目标。然而，当一个或多个潜在效应修饰因素缺失时，完全病例分析可能会导致偏差和覆盖不足。虽然已经提出并比较了处理个体随机试验中效应修饰因素数据缺失的统计方法，但对于整群随机试验设置，几乎没有相关指南，因为效应修饰因素、结局甚至缺失机制中的组内相关性可能会对异质性治疗效果的准确评估带来进一步的威胁。在本文中，通过对具有连续结局和二元效应修饰因素数据缺失的整群随机试验进行模拟研究，比较了几种缺失数据方法的性能，并使用来自工作、家庭和健康研究的真实数据进行了进一步说明。我们的结果表明，多层多重插补和贝叶斯多层多重插补比其他可用方法具有更好的性能，并且当存在模型设定或兼容性问题时，贝叶斯多层多重插补比标准多层多重插补具有更低的偏差且更接近名义覆盖率。
