Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65403
Title: Semiparametric regression analysis of multivariate longitudinal data with informative observation times
Authors: Deng, S
Liu, KY
Zhao, X 
Keywords: Estimating equation
Informative observation times
Latent variable
Model checking
Multivariate longitudinal data
Semiparametric regression
Issue Date: 2017
Publisher: North-Holland
Source: Computational statistics and data analysis, 2017, v. 107, p. 120-130 How to cite?
Journal: Computational statistics and data analysis 
Abstract: Multivariate longitudinal data arises when subjects under study may experience several possible related response outcomes. This article proposed a new class of flexible semiparametric models for multivariate longitudinal data with informative observation times through latent variables and completely unspecified link functions, which allows for any functional forms of covariate effects on the intensity functions for the observation processes. A novel estimating equation approach that does not rely on forms of link functions and distributions of frailties is developed. The asymptotic properties for the resulting estimators and the model checking technique for the overall fit of the proposed models are established. The simulation results show that the proposed approach works well. The analysis of skin cancer chemoprevention trial data is provided for illustration.
URI: http://hdl.handle.net/10397/65403
EISSN: 0167-9473
DOI: 10.1016/j.csda.2016.10.006
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