![Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5943/TOC-Subject-Sample-2021-Mathematics-Statistics.jpg"})
ABSTRACT
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.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Jijia Wang
Jijia Wang is a PhD student in the joint biostatistic program of Southern Methodist University and the University of Texas Southwestern Medical Center (UTSW).
Song Zhang
Song Zhang is an associate professor of biostatistics in the Clinical Sciences Department at UTSW.
Chul Ahn
Chul Ahn is a professor of biostatistics in the Clinical Sciences Department at UTSW.