Efficient design of cluster randomized and multicentre trials with unknown intraclass correlation

Abstract

For cluster randomized and multicentre trials evaluating the effect of a treatment on persons nested within clusters, equations have been published to compute the optimal sample sizes at the cluster and person level as a function of sampling costs and intraclass correlation ( ICC). Here, optimal means maximum power and precision for a given sampling budget, or minimum sampling costs for a given power and precision. However, the ICC is usually unknown, and the optimal sample sizes depend strongly on this ICC. To overcome this local optimality problem, this study presents Maximin designs (MMDs) based on relative efficiency (RE) and efficiency. These designs perform well over a range of possible ICC values either in terms of RE compared with the locally optimal designs, or in terms of minimum efficiency (maximum variance) of the treatment effect estimator. The use of MMDs is illustrated using information from many cluster randomized trials in primary care. It is concluded that MMDs and the optimal design for an ICC halfway its assumed range are efficient for a range of ICC values and recommendable for practical use. This requires that trial reports mention the study cost per cluster and person.