Expected estimating equations for missing data, measurement error, and misclassification, with application to longitudinal nonignorable missing data |
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Authors: | Wang C Y Huang Yijian Chao Edward C Jeffcoat Marjorie K |
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Institution: | Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, P. O. Box 19024, Seattle, Washington 98109-1024, U.S.A.;Department of Biostatistics, Emory University, Atlanta, Georgia 30322, U.S.A.;Data Numerica Institute, 6120 149th Avenue SE, Bellevue, Washington 98006, U.S.A.;School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A. |
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Abstract: | Summary . Missing data, measurement error, and misclassification are three important problems in many research fields, such as epidemiological studies. It is well known that missing data and measurement error in covariates may lead to biased estimation. Misclassification may be considered as a special type of measurement error, for categorical data. Nevertheless, we treat misclassification as a different problem from measurement error because statistical models for them are different. Indeed, in the literature, methods for these three problems were generally proposed separately given that statistical modeling for them are very different. The problem is more challenging in a longitudinal study with nonignorable missing data. In this article, we consider estimation in generalized linear models under these three incomplete data models. We propose a general approach based on expected estimating equations (EEEs) to solve these three incomplete data problems in a unified fashion. This EEE approach can be easily implemented and its asymptotic covariance can be obtained by sandwich estimation. Intensive simulation studies are performed under various incomplete data settings. The proposed method is applied to a longitudinal study of oral bone density in relation to body bone density. |
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Keywords: | Error in variable Estimating equation Latent model Misclassification Missing at random Nonignorable missing |
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