A comparison of confidence interval methods for the intraclass correlation coefficient in cluster randomized trials

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Abstract
A Correction has been published for this article in Statistics in Medicine 23(18) 2004, 2935. This study compared different methods for assigning confidence intervals to the analysis of variance estimator of the intraclass correlation coefficient (ρ). The context of the comparison was the use of ρ to estimate the variance inflation factor when planning cluster randomized trials. The methods were compared using Monte Carlo simulations of unbalanced clustered data and data from a cluster randomized trial of an intervention to improve the management of asthma in a general practice setting. The coverage and precision of the intervals were compared for data with different numbers of clusters, mean numbers of subjects per cluster and underlying values of ρ. The performance of the methods was also compared for data with Normal and non‐Normally distributed cluster specific effects. Results of the simulations showed that methods based upon the variance ratio statistic provided greater coverage levels than those based upon large sample approximations to the standard error of ρ. Searle's method provided close to nominal coverage for data with Normally distributed random effects. Adjusted versions of Searle's method to allow for lack of balance in the data generally did not improve upon it either in terms of coverage or precision. Analyses of the trial data, however, showed that limits provided by Thomas and Hultquist's method may differ from those of the other variance ratio statistic methods when the arithmetic mean differs markedly from the harmonic mean cluster size. The simulation results demonstrated that marked non‐Normality in the cluster level random effects compromised the performance of all methods. Confidence intervals for the methods were generally wide relative to the underlying size of ρsuggesting that there may be great uncertainty associated with sample size calculations for cluster trials where large clusters are randomized. Data from cluster based studies with sample sizes much larger than those typical of cluster randomized trials are required to estimate ρ with a reasonable degree of precision. Copyright © 2002 John Wiley & Sons, Ltd.