title: Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.
abstract: We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study.
authors.affiliation: Department of Biostatistics, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai 200032, China.|Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai 200032, China.
authors.forename: Huiming
authors.initials: H
authors.lastname: Lin
authors.affiliation: School of Data Science, Fudan University, Shanghai 200433, China.|Centre for Biostatistics & Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester, UK.
authors.forename: Bo
authors.initials: B
authors.lastname: Fu
authors.affiliation: Department of Biostatistics, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai 200032, China.|Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai 200032, China.
authors.forename: Guoyou
authors.identifier: 0000-0002-1413-3117
authors.initials: G
authors.lastname: Qin
authors.affiliation: Department of Statistics, Fudan University, Shanghai 200433, China.
authors.forename: Zhongyi
authors.initials: Z
authors.lastname: Zhu
mesh_terms: D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical
keywords: Doubly robust; Dropouts; Generalized partial linear models; Missing at random