Maximum likelihood analysis of generalized linear models with missing covariates

Missing data is a common occurrence in most medical research data collectio n enterprises. There is an extensive literature concerning missing data, mu ch of which has focused on missing outcomes. Covariates in regression model s are often missing, particularly if information is being collected from mu ltiple sources. The method of weights is an implementation of the EM algori thm for general maximum-likelihood analysis of regression models, including generalized linear models (GLMs) with incomplete covariates. In this paper , we will describe the method of weights in detail, illustrate its applicat ion with several examples, discuss its advantages and limitations, and revi ew extensions and applications of the method.