title: Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.
abstract: Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd.
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.initials: G
authors.lastname: Qin
authors.affiliation: Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC29208, U.S.A.
authors.forename: Jiajia
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Statistics, Fudan University, Shanghai, 200433, China.
authors.forename: Zhongyi
authors.initials: Z
authors.lastname: Zhu
authors.affiliation: Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong.
authors.forename: Wing
authors.initials: W
authors.lastname: Fung
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts; D015431:Weight Loss
keywords: dropouts; measurement error; partially linear models; regression splines; robustness