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Maximum likelihood analysis of generalized linear models with missing covariates

Harvard School of Public Health, Boston, Massachusetts, USA, horton{at}hsph.harvard.edu

Nan M Laird

Harvard School of Public Health, Boston, Massachusetts, USA

Missing data is a common occurrence in most medical research data collection enterprises. There is an extensive literature concerning missing data, much of which has focused on missing outcomes. Covariates in regression models are often missing, particularly if information is being collected from multiple sources. The method of weights is an implementation of the EM algorithm 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 application with several examples, discuss its advantages and limitations, and review extensions and applications of the method.

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