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Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study

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Title
Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study
Published in
Trials, March 2017
DOI 10.1186/s13063-017-1862-2
Pubmed ID
Authors

Daniel Barker, Catherine D’Este, Michael J. Campbell, Patrick McElduff

Abstract

Stepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated. We conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect. We found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters. Of the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.

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Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 33%
Student > Ph. D. Student 9 16%
Student > Master 7 12%
Professor > Associate Professor 4 7%
Student > Doctoral Student 4 7%
Other 6 10%
Unknown 9 16%
Readers by discipline Count As %
Medicine and Dentistry 19 33%
Nursing and Health Professions 7 12%
Social Sciences 5 9%
Mathematics 5 9%
Psychology 2 3%
Other 10 17%
Unknown 10 17%