Methods: Mind the Gap

Webinar Series

Multiple Imputation Methods for Group-Based Interventions

June 17, 2022
Rebecca Andridge
Rebecca Andridge, Ph.D.

The Ohio State University

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About the Webinar

Many types of interventions are delivered in groups, and randomized trial designs to evaluate such interventions include cluster randomized trials (CRTs), individually randomized group treatment trials (IRGTs), and stepped wedge (SW) CRTs. Resulting data usually consist of a small number of clusters with correlated observations within a treatment group. Missing data often present a problem in the analysis of such trials; multiple imputation (MI) is an attractive approach, as it results in complete data sets that can be analyzed with well-established analysis methods for clustered designs. However, care must be taken when implementing MI to properly account for the within-cluster correlation. In this presentation, Dr. Rebecca Andridge reviews proper strategies for imputation in clustered designs, including the illustration of implementations in software.

About Rebecca Andridge

Dr. Rebecca Andridge is an Associate Professor in the Division of Biostatistics in the Ohio State University College of Public Health. Her research is focused on imputation methods for missing data, primarily in large-scale probability samples and group randomized trials. Dr. Andridge focuses on methods for imputing data when missingness is driven by the missing values themselves (missing not at random). She collaborates with researchers across campus, including the Institute for Behavioral Medicine Research; the Nisonger Center, a University Center for Excellence in Developmental Disabilities; and the Comprehensive Cancer Center. In addition, Dr. Andridge teaches introductory graduate and undergraduate biostatistics, and was the 2011 winner of the College of Public Health Excellence in Teaching Award. She is an elected Fellow of the American Statistical Association.

Last updated on July 1, 2022