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Journal of Probability and Statistics
Volume 2012, Article ID 734341, 15 pages
Research Article

A Semiparametric Marginalized Model for Longitudinal Data with Informative Dropout

1Division of Biostatistics, Department of Environmental Medicine, New York University School of Medicine, New York, NY 10016, USA
2Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA

Received 1 April 2011; Revised 11 July 2011; Accepted 27 July 2011

Academic Editor: Yangxin Huang

Copyright © 2012 Mengling Liu and Wenbin Lu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a marginalized joint-modeling approach for marginal inference on the association between longitudinal responses and covariates when longitudinal measurements are subject to informative dropouts. The proposed model is motivated by the idea of linking longitudinal responses and dropout times by latent variables while focusing on marginal inferences. We develop a simple inference procedure based on a series of estimating equations, and the resulting estimators are consistent and asymptotically normal with a sandwich-type covariance matrix ready to be estimated by the usual plug-in rule. The performance of our approach is evaluated through simulations and illustrated with a renal disease data application.