Measures of between-cluster variability in cluster randomized trials with binary outcomes.
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.
Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, U.K. andrew.thomson@mhra.gsi.gov.uk
Andrew
A
Thomson
Richard
R
Hayes
Simon
S
Cousens
D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012306:Risk; D018401:Sample Size; D016896:Treatment Outcome