• Stefan Wellek, Norbert Donner-Banzhoff, Jochem König, Philipp Mildenberger, and Maria Blettner.
• Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), Faculty of Medicine, Johannes Gutenberg University of Mainz; Institute for Medical Biostatistics, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany; Department of General Practice/Family Medicine, University of Marburg; Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), Faculty of Medicine, Johannes Gutenberg University of Mainz; Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), Faculty of Medicine, Johannes Gutenberg University of Mainz; Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), Faculty of Medicine, Johannes Gutenberg University of Mainz.
• Dtsch Arztebl Int. 2019 Jun 28; 116 (26): 453458453-458.

BackgroundThe stepped-wedge design (SWD) of clinical trials has become very popular in recent years, particularly in health services research. Typically, study participants are randomly allotted in clusters to the different treatment options.MethodsThe basic principles of the stepped wedge design and related statistical techniques are described here on the basis of pertinent publications retrieved by a selective search in PubMed and in the CIS statistical literature database.ResultsIn a typical SWD trial, the intervention is begun at a time point that varies from cluster to cluster. Until this time point is reached, all participants in the cluster belong to the control arm of the trial. Once the intervention is begun, it is continued with- out change until the end of the trial period. The starting time for the intervention in each cluster is determined by randomization. At the first time point of measurement, no intervention has yet begun in any cluster; at the last one, the intervention is in prog- ress in all clusters. The treatment effect can be optimally assessed under the assumption of an identical correlation at all time points. A method is available to calculate the power and the number of clusters that would be necessary in order to achieve statistical significance by the appropriate type of significance test. All of the statistical techniques presented here are based on the assumptions of a normal distribution of cluster means and of a constant intervention effect across all time points of measure- ment.ConclusionThe necessary statistical tools for the planning and evaluation of SWD trials now stand at our disposal. Such trials nevertheless are subject to major risks, as valid results can be obtained only if the far-reaching assumptions of the model are, in fact, justified.

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