For CRTs, the stepped wedge design is far more efficient than the parallel group and ANCOVA design in terms of sample size.

The sample size required for a cluster randomised trial is inflated compared with an individually randomised trial because outcomes of participants from the same cluster are correlated. Sample size calculations for longitudinal cluster randomised trials (including stepped wedge trials) need to take account of at least two levels of clustering: the clusters themselves and times within clusters. We derive formulae for sample size for repeated cross-section and closed cohort cluster randomised trials with normally distributed outcome measures, under a multilevel model allowing for variation between clusters and between times within clusters. Our formulae agree with those previously described for special cases such as crossover and analysis of covariance designs, although simulation suggests that the formulae could underestimate required sample size when the number of clusters is small. Whether using a formula or simulation, a sample size calculation requires estimates of nuisance parameters, which in our model include the intracluster correlation, cluster autocorrelation, and individual autocorrelation. A cluster autocorrelation less than 1 reflects a situation where individuals sampled from the same cluster at different times have less correlated outcomes than individuals sampled from the same cluster at the same time. Nuisance parameters could be estimated from time series obtained in similarly clustered settings with the same outcome measure, using analysis of variance to estimate variance components. Copyright © 2016 John Wiley & Sons, Ltd.

For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An analysis of covariance (ANCOVA) using both the baseline and follow-up score of the outcome will then have more power. We calculate the efficiency of an ANCOVA analysis using the baseline scores compared with an analysis on follow-up scores only. The sample size for such an ANCOVA analysis is a factor r2 smaller, where r is the correlation of the cluster means between baseline and follow-up. This correlation can be expressed in clinically interpretable parameters: the correlation between baseline and follow-up of subjects (subject autocorrelation) and that of clusters (cluster autocorrelation). Because of this, subject matter knowledge can be used to provide (range of) plausible values for these correlations, when estimates from previous studies are lacking. Depending on how large the subject and cluster autocorrelations are, analysis of covariance can substantially reduce the number of clusters needed.

BB results in a better balance of prognostic factors than randomization, minimization, stratification, and matching in most situations. Furthermore, BB cannot result in a worse balance of prognostic factors than the other methods.

BackgroundVarious papers have addressed pros and cons of the stepped wedge cluster randomized trial design (SWD). However, some issues have not or only limitedly been addressed. Our aim was to provide a comprehensive overview of all merits and limitations of the SWD to assist researchers, reviewers and medical ethics committees when deciding on the appropriateness of the SWD for a particular study.MethodsWe performed an initial search to identify articles with a methodological focus on the SWD, and categorized and discussed all reported advantages and disadvantages of the SWD. Additional aspects were identified during multidisciplinary meetings in which ethicists, biostatisticians, clinical epidemiologists and health economists participated. All aspects of the SWD were compared to the parallel group cluster randomized design. We categorized the merits and limitations of the SWD to distinct phases in the design and conduct of such studies, highlighting that their impact may vary depending on the context of the study or that benefits may be offset by drawbacks across study phases. Furthermore, a real-life illustration is provided.ResultsNew aspects are identified within all disciplines. Examples of newly identified aspects of an SWD are: the possibility to measure a treatment effect in each cluster to examine the (in)consistency in effects across clusters, the detrimental effect of lower than expected inclusion rates, deviation from the ordinary informed consent process and the question whether studies using the SWD are likely to have sufficient social value. Discussions are provided on e.g. clinical equipoise, social value, health economical decision making, number of study arms, and interim analyses.ConclusionsDeciding on the use of the SWD involves aspects and considerations from different disciplines not all of which have been discussed before. Pros and cons of this design should be balanced in comparison to other feasible design options as to choose the optimal design for a particular intervention study.