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Abstract:
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Longitudinal cluster-randomization design has been frequently used in pragmatic clinical trials. It features in three-level hierarchical structure with multiple observations per participant nested within a cluster. The existing literature on statistical methods have focused on the two-level or three-level hierarchical structure without longitudinally measured data. The methodologies for the three-level hierarchical design are not well understood, especially when the study interest is in the interaction effect between treatment and time. Additional challenges include a small number of available clusters, the missing data in longitudinal outcomes, and the imbalanced marker distributions among clusters. Our study focuses on two aspects of the three-level hierarchical design. First, we evaluate the performance of several sandwich type variance estimators on the inference of treatment-by-time interaction given a small number of clusters. Second, we attempt to determine the optimal balance between the number of clusters and cluster sizes under budget constraints using a simulation-based method. The proposed approach is illustrated using the study of adults with serious mental Illness.
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