Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information.
This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general method for estimating generalized linear regression models with missing covariates using the EM algorithm that is easily implemented when there is no auxiliary data. Vach (1997, Statistics in Medicine 16, 57-72) describes how the method can be extended when the outcome and auxiliary data are conditionally independent given the covariates in the model. The method allows the incorporation of auxiliary data without making the conditional independence assumption. We suggest tests of conditional independence and compare the performance of several estimators in an example concerning mental health service utilization in children. Using an artificial dataset, we compare the performance of several estimators when auxiliary data are available.
Department of Epidemiology and Biostatistics, Boston University School of Public Health, Massachusetts 02118, USA. horton@bu.edu
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D000704:Analysis of Variance; D001699:Biometry; D002648:Child; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008605:Mental Health Services / Q000706:statistics & numerical data