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	Joint analysis of longitudinal data with additive mixed effect model for informative observation times
	Fang, Sha ; Zhang, Haixiang ; Sun, Liuquan
	2016
关键词	Additive mixed effect model Estimating equations Informative observation times Joint modeling Latent variables Longitudinal data RECURRENT EVENT DATA DEPENDENT FOLLOW-UP REGRESSION-ANALYSIS SEMIPARAMETRIC REGRESSION
英文摘要	Longitudinal data occur in many clinical and observational studies, and in many situations, longitudinal responses are often correlated with observation times. In this article, we propose a new joint model for the analysis of longitudinal data with informative observation times via two random effects. In particular, an additive mixed effect model is used for observation times. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are provided for model checking. The finite-sample behavior of the proposed method is evaluated through simulation studies, and an application to a bladder cancer study is illustrated. (C) 2015 Elsevier B.V. All rights reserved.; National Natural Science Foundation of China [11231010, 11171330, 11301212]; China Postdoctoral Science Foundation [2014M550861]; Key Laboratory of RCSDS, CAS [2008DP173182]; SCI(E); ARTICLE; slq@amt.ac.cn; 43-55; 169
语种	英语
出处	SCI
出版者	JOURNAL OF STATISTICAL PLANNING AND INFERENCE
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/438757]
专题	数学科学学院
推荐引用方式 GB/T 7714	Fang, Sha,Zhang, Haixiang,Sun, Liuquan. Joint analysis of longitudinal data with additive mixed effect model for informative observation times. 2016-01-01.

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