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Yongdong Ouyang
Assistant Professor

Curriculum vitae


Biostatistics and Bioinformatics

Roswell Park Comprehensive Cancer Center

RSC 424, Elm & Carlton St, Buffalo, New York, USA



Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison.


Journal article


Yongdong Ouyang, Fan Li, J. Preisser, M. Taljaard
International Journal of Epidemiology, 2022

Semantic Scholar DOI PubMed
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Cite

APA   Click to copy
Ouyang, Y., Li, F., Preisser, J., & Taljaard, M. (2022). Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison. International Journal of Epidemiology.


Chicago/Turabian   Click to copy
Ouyang, Yongdong, Fan Li, J. Preisser, and M. Taljaard. “Sample Size Calculators for Planning Stepped-Wedge Cluster Randomized Trials: a Review and Comparison.” International Journal of Epidemiology (2022).


MLA   Click to copy
Ouyang, Yongdong, et al. “Sample Size Calculators for Planning Stepped-Wedge Cluster Randomized Trials: a Review and Comparison.” International Journal of Epidemiology, 2022.


BibTeX   Click to copy

@article{yongdong2022a,
  title = {Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison.},
  year = {2022},
  journal = {International Journal of Epidemiology},
  author = {Ouyang, Yongdong and Li, Fan and Preisser, J. and Taljaard, M.}
}

Abstract

Recent years have seen a surge of interest in stepped-wedge cluster randomized trials (SW-CRTs). SW-CRTs include several design variations and methodology is rapidly developing. Accordingly, a variety of power and sample size calculation software for SW-CRTs has been developed. However, each calculator may support only a selected set of design features and may not be appropriate for all scenarios. Currently, there is no resource to assist researchers in selecting the most appropriate calculator for planning their trials. In this paper, we review and classify 18 existing calculators that can be implemented in major platforms, such as R, SAS, Stata, Microsoft Excel, PASS and nQuery. After reviewing the main sample size considerations for SW-CRTs, we summarize the features supported by the available calculators, including the types of designs, outcomes, correlation structures and treatment effects; whether incomplete designs, cluster-size variation or secular trends are accommodated; and the analytical approach used. We then discuss in more detail four main calculators and identify their strengths and limitations. We illustrate how to use these four calculators to compute power for two real SW-CRTs with a continuous and binary outcome and compare the results. We show that the choice of calculator can make a substantial difference in the calculated power and explain these differences. Finally, we make recommendations for implementing sample size or power calculations using the available calculators. An R Shiny app is available for users to select the calculator that meets their requirements (https://douyang.shinyapps.io/swcrtcalculator/).



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