Background
Continence, Stroke
WASTED, STROKE
Health interventions are often evaluated using cluster-randomised trials where clusters of individuals are randomly allocated to trial arms.
Similarities among individuals within clusters cause a loss of statistical power to detect a between-group difference. To maintain power, the target sample size must be inflated to allow for clustering: the sample size of an individually randomised trial is multiplied by the design effect DEff = 1 + (As sample size is highly sensitive to the ICC, robust ICC estimates are required. Currently, obtaining a robust estimate of the ICC is a challenge in planning a cluster-randomised trial.The usual, and easiest, way of obtaining an ICC is using a single estimate from an existing source, such as a published similar study, published lists or databases of ICC estimates, or a pilot for the proposed study. Use of a single ICC value is not robust (often resulting in a trial being underpowered or overpowered causing wasted resources) and a sufficiently relevant value is seldom available.An advanced approach might use multiple-estimate methods, combining different ICC estimates for the particular outcome and cluster type. However, finding multiple sources highly relevant to the target outcome, population and cluster type is quite difficult. It is likely that available studies with ICCs will have only partial relevance to the planned trial. Furthermore, there is no recommended method of combining ICCs and simple approaches, such as using their mean, median or maximum, do not account for the specific characteristics of the studies, the degree of relevance or the uncertainty in each ICC estimate.Imprecision in the ICC is usually expressed in terms of variance of the estimate, with different ways of calculating ICC variance proposed.In this study, we adopted and extended the method of combining ICC values in the Bayesian frameworkWe extended the approach suggested in Turner et al.A robust ICC estimate was not available to inform the proposed trial, although a feasibility trial, Identifying Continence OptioNs after Stroke (ICONS),We applied a Bayesian approach to modelling and estimation of the ICC for determination of the sample size using multiple ICC estimates from previously published studies. The modelling automatically accounted for uncertainty in the synthesised ICCs and produced an informative posterior ICC distribution which we used to determine the sample size. We describe here the process used and provide a how-to guide on the proposed framework to assist researchers in exploring the utility of this approach for their own trials.