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Power analysis for cluster randomized trials with multiple binary co‐primary endpoints

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Listed:
  • Dateng Li
  • Jing Cao
  • Song Zhang

Abstract

Cluster randomized trials (CRTs) are widely used in different areas of medicine and public health. Recently, with increasing complexity of medical therapies and technological advances in monitoring multiple outcomes, many clinical trials attempt to evaluate multiple co‐primary endpoints. In this study, we present a power analysis method for CRTs with K≥2 binary co‐primary endpoints. It is developed based on the GEE (generalized estimating equation) approach, and three types of correlations are considered: inter‐subject correlation within each endpoint, intra‐subject correlation across endpoints, and inter‐subject correlation across endpoints. A closed‐form joint distribution of the K test statistics is derived, which facilitates the evaluation of power and type I error for arbitrarily constructed hypotheses. We further present a theorem that characterizes the relationship between various correlations and testing power. We assess the performance of the proposed power analysis method based on extensive simulation studies. An application example to a real clinical trial is presented.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:4:p:1064-1074
    DOI: 10.1111/biom.13212
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    References listed on IDEAS

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    1. repec:aph:ajpbhl:10.2105/ajph.2017.303707_9 is not listed on IDEAS
    2. 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.
    3. repec:aph:ajpbhl:10.2105/ajph.2017.303706_3 is not listed on IDEAS
    4. Murray, D.M. & Varnell, S.P. & Blitstein, J.L., 2004. "Design and Analysis of Group-Randomized Trials: A Review of Recent Methodological Developments," American Journal of Public Health, American Public Health Association, vol. 94(3), pages 423-432.
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    1. Ryan Thompson & Catherine S. Forbes & Steven N. MacEachern & Mario Peruggia, 2022. "Familial Inference," Monash Econometrics and Business Statistics Working Papers 2/22, Monash University, Department of Econometrics and Business Statistics.

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