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Activity Details


CE_16C Mon, 8/4/2014, 8:30 AM - 5:00 PM CC-161
Missing Data Methods for Regression Modeling — Professional Development Continuing Education Course
ASA , Biometrics Section
This short course covers a very important topic in statistical inference, namely missing data methods in regression models. Missing data is a major issue in many applied problems, especially in the biomedical sciences, including clinical trials, longitudinal studies, observational studies, and sample surveys. The short course on such a topic is timely, since much software has been recently developed to fit various types of regression models with missing covariate and/or response data. One unique and extremely strong feature in this short course is that it will focus on regression models and research problems encountered in actual practice and it will demonstrate a wide variety of statistical packages dealing with missing data including SAS, logXact, and WinBUGS. Regression models covered will include linear and generalized linear models, models for longitudinal data, and survival models. Missing responses and/or covariates will be examined as well as ignorable and nonignorable missing data mechanisms. This short course will be quite comprehensive in its coverage of the various methodologies for handling missing data, including detailed coverage of Maximum Likelihood (ML), Multiple Imputation (MI), Fully Bayesian (FB) methods, and Weighted Estimating Equations (WEE). We will examine several case studies with missing data and demonstrate the various missing data methodologies using these case studies.
Instructor(s): Joseph Ibrahim, University of North Carolina



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