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Methods: Mind the Gap

Webinar Series

Overview of Statistical Models for the Design and Analysis of Stepped Wedge Cluster Randomized Trials

July 14, 2020
Fan Li Headshot
Fan Li, Ph.D.

Yale University School of Public Health 

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About the Webinar

The stepped wedge cluster randomized design has received increasing attention in pragmatic clinical trials and implementation science research. The key feature of the design is the unidirectional crossover of clusters from the control to intervention conditions on a staggered schedule, which induces confounding of the intervention effect by time. The stepped wedge design first appeared in the Gambia Hepatitis Intervention Study in the 1980s. However, the statistical model used for the design and analysis was not formally introduced until 2007 in an article by Michael A. Hussey and James P. Hughes. Since then, a variety of mixed-effects model extensions have been proposed for the design and analysis of these trials.

In this talk, Dr. Fan Li explores these extensions under a unified perspective. He provides a general model representation and regard various model extensions as alternative ways to characterize the secular trend, intervention effect, and sources of heterogeneity. He reviews the key model ingredients and clarify their implications for the design and analysis of such trials. This talk may serve as an entry point to understanding the evolving statistical literatures on stepped wedge designs.

About Fan Li

Dr. Li is an Assistant Professor of Biostatistics at the Department of Biostatistics, Yale University School of Public Health. He is also a faculty member at the Yale Center for Methods in Implementation and Prevention Science, and Yale Center for Analytical Sciences. He has a Ph.D. in biostatistics from Duke University, and his main research interests include methods for designing and analyzing cluster randomized trials, methods for addressing missing data, and methods for estimating causal effects with observational studies. 

As a biostatistician, Dr. Li works with researchers from a variety of medical specialties in the design and analysis of cluster randomized trials, individually randomized clinical trials, and observational studies. He is an active member of the Biostatistics and Study Design Core in the National Institutes of Health Collaboratory of Pragmatic Clinical Trials, and an executive member of the Design and Statistics Core in the National Institute on Aging (NIA) IMbedded Pragmatic Alzheimer’s Disease (AD) and AD Related Dementias (AD/ADRD) Clinical Trials (IMPACT) Collaboratory. He also supervises M.S. and Ph.D. students in biostatistics and teaches a course on causal inference methods at Yale University School of Public Health.

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