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Understanding whether and how treatment effects vary across subgroups iscrucial to inform clinical practice and recommendations. Accordingly, theassessment of heterogeneous treatment effects (HTE) based on pre-specifiedpotential effect modifiers has become a common goal in modern randomizedtrials. However, when one or more potential effect modifiers are missing,complete-case analysis may lead to bias and under-coverage. While statisticalmethods for handling missing data have been proposed and compared forindividually randomized trials with missing effect modifier data, fewguidelines exist for the cluster-randomized setting, where intraclustercorrelations in the effect modifiers, outcomes, or even missingness mechanismsmay introduce further threats to accurate assessment of HTE. In this article,the performance of several missing data methods are compared through asimulation study of cluster-randomized trials with continuous outcome andmissing binary effect modifier data, and further illustrated using real datafrom the Work, Family, and Health Study. Our results suggest that multilevelmultiple imputation (MMI) and Bayesian MMI have better performance than otheravailable methods, and that Bayesian MMI has lower bias and closer to nominalcoverage than standard MMI when there are model specification or compatibilityissues.

Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials