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A behavioural Bayes approach for sample size determination in cluster randomized clinical trials

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  • Takashi Kikuchi
  • John Gittins

Abstract

Summary. Cluster randomized clinical trials are increasingly popular to evaluate disease control interventions for communities. In these trials health interventions are allocated randomly to complete clusters or groups rather than to individual subjects. Sample size calculation for cluster randomized clinical trials has been largely based on classical theory, taking account of between‐cluster variation, and of type I and II errors. It is desirable to use an approach which maximizes the expected net benefit, but there is as yet no established methodology along these lines. Gittins and Pezeshk presented an expected net benefit approach to sample size determination. We extend that approach to cluster randomized clinical trials.

Suggested Citation

  • Takashi Kikuchi & John Gittins, 2010. "A behavioural Bayes approach for sample size determination in cluster randomized clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 875-888, November.
    • Handle: RePEc:bla:jorssc:v:59:y:2010:i:5:p:875-888
    DOI: 10.1111/j.1467-9876.2010.00732.x
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    References listed on IDEAS

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    1. Connelly, Luke B., 2003. "Balancing the Number and Size of Sites: An Economic Approach to the Optimal Design of Cluster Samples," MPRA Paper 14676, University Library of Munich, Germany.
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    Cited by:

    1. Alan J. Girling & Richard J. Lilford & Terry P. Young, 2012. "Pricing Of Medical Devices Under Coverage Uncertainty—A Modelling Approach," Health Economics, John Wiley & Sons, Ltd., vol. 21(12), pages 1502-1507, December.

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