title: Generalized partially linear models for incomplete longitudinal data in the presence of population-level information.
abstract: In observational studies, interest often lies in estimation of the population-level relationship between the explanatory variables and dependent variables, and the estimation is often done using longitudinal data. Longitudinal data often feature sampling error and bias due to nonrandom drop-out. However, inclusion of population-level information can increase estimation efficiency. In this article, we consider a generalized partially linear model for incomplete longitudinal data in the presence of the population-level information. A pseudo-empirical likelihood-based method is introduced to incorporate population-level information, and nonrandom drop-out bias is corrected by using a weighted generalized estimating equations method. A three-step estimation procedure is proposed, which makes the computation easier. Several methods that are often used in practice are compared in simulation studies, which demonstrate that our proposed method can correct the nonrandom drop-out bias and increase the estimation efficiency, especially for small sample size or when the missing proportion is high. We apply this method to an Alzheimer's disease study.
authors: Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA.
authors: Baojiang
authors: B
authors: Chen
authors: Xiao-Hua
authors: XH
authors: Zhou
mesh_terms: D000544:Alzheimer Disease / Q000453:epidemiology / Q000209:etiology; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical