Power analysis for stratified cluster randomisation trials with cluster size being the stratifying factor

Stratified cluster randomisation trial design is widely employed in biomedical research and cluster size has been frequently used as the stratifying factor.Conventional sample size calculation methods have assumed the cluster sizes to be constant within each stratum,which is rarely true in practice.Ignoring the random variability in cluster size leads to underestimated sample sizes and underpowered clinical trials.In this study,we proposed to directly incorporate the variability in cluster size(represented by coefficient of variability)into sample size calculation.This approach provides closed-form sample size formulas,and is flexible to accommodate arbitrary randomisation ratio and varying numbers of clusters across strata.Simulation study shows that the proposed approach achieves desired power and type I error over a wide spectrum of design configurations,including different distributions of cluster sizes.An application example is presented.

Sample size and power analysis for stepped wedge cluster randomised trials with binary outcomes

In stepped wedge cluster randomised trials(SW-CRTs),clusters of subjects are randomly assigned to sequences,where they receive a specific order of treatments.Compared to conventional cluster randomised studies,one unique feature of SW-CRTs is that all clusters start from control and gradually transition to intervention according to the randomly assigned sequences.This feature mitigates the ethical concern of withholding an effective treatment and reduces the logistic burden of implementing the intervention at multiple clusters simultaneously.This feature,however,presents challenges that need to be addressed in experimental design and data analysis,i.e.,missing data due to prolonged follow-up and complicated correlation structures that involve between-subject and longitudinal correlations.In this study,based on the generalised estimating equation(GEE)approach,we present a closed-form sample size formula for SW-CRTs with a binary outcome,which offers great flexibility to account for unbalanced randomisation,missing data,and arbitrary correlation structures.We also present a correction approach to address the issue of under-estimated variance by GEE estimator when the sample size is small.Simulation studies and application to a real clinical trial are presented.