Journal
BIOSTATISTICS
Volume 24, Issue 4, Pages 833-849Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxac026
Keywords
Design effect; Effect modification; Heterogeneous treatment effect; Intraclass correlation coefficient; Nested exchangeable correlation structure; Power calculation
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This article explores the heterogeneity of treatment effects in different patient subpopulations in cluster randomized trials and presents a novel analytical design formula that can be widely applied to evaluate effect modifiers at different levels. The effectiveness of this new method is validated through simulation studies and real-world trial examples.
Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among prespecified patient subpopulations, such as those defined by demographics or baseline clinical characteristics. In this article, we derive novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level, and participant-level effect modifiers and to designs where randomization can be carried out at any level. We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and generate new insights from a study design perspective for conducting analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model. A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations.
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