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Sample size estimation in cluster randomized trials: An evidence-based perspective


  • Rotondi, Michael
  • Donner, Allan


The evidence-based perspective to sample size estimation determines appropriate trial size by examining its potential impact on the literature. This approach is extended to determine the appropriate size of a planned cluster randomized trial by considering the role of the planned trial on a future meta-analysis (including current literature and the proposed study). A simulation-based algorithm allows consideration of variable cluster sizes and intracluster correlation coefficient values in conjunction with three approaches to sample size estimation, namely the power-based, variance reduction and non-inferiority perspectives. Two examples employing the sample size estimation techniques described are discussed in detail, while appropriate code is provided in the accompanying R package CRTSize.

Suggested Citation

  • Rotondi, Michael & Donner, Allan, 2012. "Sample size estimation in cluster randomized trials: An evidence-based perspective," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1174-1187.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1174-1187 DOI: 10.1016/j.csda.2010.12.010

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    References listed on IDEAS

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    5. Yi Qian, 2007. "Do National Patent Laws Stimulate Domestic Innovation in a Global Patenting Environment? A Cross-Country Analysis of Pharmaceutical Patent Protection, 1978-2002," The Review of Economics and Statistics, MIT Press, vol. 89(3), pages 436-453, August.
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