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Ibrahim, Joseph G.; Chen, Ming-Hui; Lipsitz, Stuart R.; & Herring, Amy H. (2005). Missing Data Methods for Generalized Linear Models: A Comparative Review. Journal of the American Statistical Association, 100(469), 332-346.


Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate data: maximum likelihood (ML), multiple imputation (MI), fully Bayesian (FB), and weighted estimating equations (WEEs). There is considerable interest in how these four methodologies are related, the properties of each approach, the advantages and disadvantages of each methodology, and computational implementation. We examine data that are missing at random and nonignorable missing. For ML, we focus on techniques using the EM algorithm, and in particular, discuss the EM by the method of weights and related procedures as discussed by Ibrahim. For MI, we examine the techniques developed by Rubin. For FB, we review approaches considered by Ibrahim et al. For WEE, we focus on the techniques developed by Robins et al. We use a real dataset and a detailed simulation study to compare the four methods.


Reference Type

Journal Article

Year Published


Journal Title

Journal of the American Statistical Association


Ibrahim, Joseph G.
Chen, Ming-Hui
Lipsitz, Stuart R.
Herring, Amy H.