Yale School of Public Health
About the Webinar
The stepped wedge design is increasingly popular in a wide variety of settings, including public health intervention evaluations and clinical and health service research. Previous studies presenting power calculation methods for stepped wedge designs have focused on continuous outcomes and relied on normal approximations for binary outcomes. However, the approximation method may not be accurate. Dr. Xin Zhou introduces two new methods, using maximum likelihood and generalized estimating equations, to improve the power calculation for binary outcomes. Dr. Zhou has also developed user-friendly software, including a SAS macro, an R package, and a Shiny app, for power calculations in stepped wedge designs. In this presentation, uses the R package to show how to use the software for power calculations in stepped wedge designs.
About Xin Zhou
Dr. Xin Zhou is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. He received his Ph.D. in biostatistics from the University of North Carolina at Chapel Hill. Prior to arriving at Yale, Dr. Zhou was a Postdoctoral Fellow in the Departments of Biostatistics and Epidemiology at Harvard T.H. Chan School of Public Health. His research focuses on cluster randomized trials, measurement error correction, and statistical and machine learning methods in precision medicine.