Pragmatic solutions for longitudinal cluster randomized trials

Longitudinal cluster randomized trial (LCRT) is one type of cluster randomized trials, which has been frequently used in clinical research. In LCRTs, clusters of subjects are randomly assigned to different treatment groups or sequences with various treatment orders, and each subject has repeated measurements over the time during the study. These features, however, present challenges that need to be addressed in both experimental design and data analysis stages. Two salient features of LCRTs are the complicated correlation structure constituted by longitudinal and between-subject correlations and the missing scenarios caused by the prolonged study period. To handle them, we propose closed-form sample size and power formulas for detecting the intervention effect for LCRTs with different types of outcomes and distinct design features, which offer great flexibility to account for unbalanced design, various design matrices, different missing patterns, and complicated correlation structures. Extensive simulation studies showed that the proposed methods achieve good performance over a wide spectrum of design configurations.

Longitudinal study

Cluster

correlation

missing

power analysis 

Mid-Level

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2023