title: Maximum likelihood analysis of generalized linear models with missing covariates.
abstract: 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.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. horton@hsph.harvard.edu
authors.forename: N J
authors.initials: NJ
authors.lastname: Horton
authors.forename: N M
authors.initials: NM
authors.lastname: Laird
mesh_terms: D000465:Algorithms; D002648:Child; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D008605:Mental Health Services / Q000706:statistics & numerical data; D015233:Models, Statistical; D009010:Monte Carlo Method