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

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  • Jijia Wang
  • Song Zhang
  • Chul Ahn

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.

Suggested Citation

  • Jijia Wang & Song Zhang & Chul Ahn, 2017. "Power analysis for stratified cluster randomisation trials with cluster size being the stratifying factor," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 1(1), pages 121-127, January.
    • Handle: RePEc:taf:tstfxx:v:1:y:2017:i:1:p:121-127
    DOI: 10.1080/24754269.2017.1347309
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    Cited by:

    1. Dateng Li & Jing Cao & Song Zhang, 2020. "Power analysis for cluster randomized trials with multiple binary co‐primary endpoints," Biometrics, The International Biometric Society, vol. 76(4), pages 1064-1074, December.

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