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Measures of between-cluster variability in cluster randomized trials with binary outcomes
Andrew Thomson1, Richard Hayes, Simon Cousens
1Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, U.K. andrew.thomson@mhra.gsi.gov.uk
Abstract:
Cluster randomized trials (CRTs) are increasingly used to evaluate the effectiveness of health-care interventions. A key feature of CRTs is that the observations on individuals within clusters are correlated as a result of between-cluster variability. Sample size formulae exist which account for such correlations, but they make different assumptions regarding the between-cluster variability in the intervention arm of a trial, resulting in different sample size estimates. We explore the relationship for binary outcome data between two common measures of between-cluster variability: k, the coefficient of variation and rho, the intracluster correlation coefficient. We then assess how the assumptions of constant k or rho across treatment arms correspond to different assumptions about intervention effects. We assess implications for sample size estimation and present a simple solution to the problems outlined.
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