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Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts
Author(s) -
Lin Huiming,
Fu Bo,
Qin Guoyou,
Zhu Zhongyi
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12703
Subject(s) - estimation , longitudinal data , statistics , mathematics , generalized linear model , econometrics , computer science , data mining , engineering , systems engineering
Summary 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.