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Constructing intervals for the intracluster correlation coefficient using Bayesian modelling, and application in cluster randomized trials

Turner, RA; Omar, RZ; Thompson, SG; (2006) Constructing intervals for the intracluster correlation coefficient using Bayesian modelling, and application in cluster randomized trials. STAT MED , 25 (9) 1443 - 1456. 10.1002/sim.2304.

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Abstract

Studies in health research are commonly carried out in clustered settings, where the individual response data are correlated within clusters. Estimation and modelling of the extent of between-cluster variation contributes to understanding of the current study and to design of future studies. It is common to express between-cluster variation as an intracluster correlation coefficient (ICC), since this measure is directly comparable across outcomes. ICCs are generally reported unaccompanied by confidence intervals. In this paper, we describe a Bayesian modelling approach to interval estimation of the ICC. The flexibility of this framework allows useful extensions which are not easily available in existing methods, for example assumptions other than Normality for continuous outcome data, adjustment for individual-level covariates and simultaneous interval estimation of several ICCs. There is also the opportunity to incorporate prior beliefs on likely values of the ICC. The methods are exemplified using data from a cluster randomized trial. Copyright (c) 2005 John Wiley & Sons, Ltd.

Type:Article
Title:Constructing intervals for the intracluster correlation coefficient using Bayesian modelling, and application in cluster randomized trials
DOI:10.1002/sim.2304
Keywords:intracluster correlation coefficient, clustered data, cluster randomized trials, interval estimation, Bayesian methods, INTRACLASS CORRELATION-COEFFICIENT, BINARY OUTCOME DATA, SAMPLE-SIZE, ESTIMATING EQUATIONS, PRIMARY-CARE, DESIGN, VARIANCE, PREVENTION, ESTIMATOR, PROFILES
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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