Modeling Longitudinal Categorical Response Data- Maura Stokes, SAS

Longitudinal data occurs for responses that represent binary and multinomial outcomes as well as counts. This data is commonly correlated and often includes missing values, so any analysis needs to take both of these factors into consideration. This tutorial focuses on using generalized estimating equations for analyzing longitudinal categorical response data, but it also discusses the generalized linear mixed models approach. Strategies such as weighted generalized estimating equations for managing missing data are also discussed, along with the assumptions for these methods. Techniques are illustrated with real-world applications using SAS® procedures such as GENMOD, GLIMMIX, and the GEE procedure.

About the Presenter

Maura E. Stokes is a Senior R&D Director at SAS Institute. She received her DrPH in biostatistics from the University of North Carolina at Chapel Hill and has taught and written about categorical data analysis for over twenty-five years. She is a Fellow of the American Statistical Association.