title: Doubly robust estimates for binary longitudinal data analysis with missing response and missing covariates.
abstract: Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the expectation-maximization algorithm give consistent estimators for model parameters when data are missing at random (MAR) provided that the response model and the missing covariate model are correctly specified; however, we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation, which gives consistent estimators if the missing data and response models are correctly specified; however, we do not need to specify the distribution of the covariates that have missing values. In this article, we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are MAR. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations.
authors.affiliation: Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA. baojiang.chen@unmc.edu
authors.forename: Baojiang
authors.initials: B
authors.lastname: Chen
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical