Bayesian varying coefficient mixed-effects joint models with asymmetry and missingness. (June 2017)

• Record Type: Journal Article
• Authors: Lu, Tao; Cai, Chunyan; Lu, Minggen; Zhang, Jun; Dong, Guang-Hui; Wang, Min
• Is Part Of: Statistical modelling. Volume 17:Number 3(2017)
• Page Start: 117
• Page End: 141
• Publication Date: 2017-06
• Subjects: Bayesian inference -- Competing risks -- longitudinal data -- varying coefficient mixed-effects models -- missing data -- proportional hazard models -- asymmetric distribution -- survival data; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X16678543
• Languages: English
• ISSNs: 1471-082X

Abstract

Abstract: Longitudinal and survival data are often collected from clinical studies. Mixed-effects joint models are commonly used for the analysis of such data. Nevertheless, the following issues may arise in longitudinal survival data analysis: (a) most joint models assume a simple parametric mixed-effects model for longitudinal outcome, which may obscure the important relationship between response and covariates; (b) clinical data often exhibits asymmetry so that symmetric assumption for model errors may lead to biased estimation of parameters; (c) response may be missing and missingness may be informative. There is little work concerning all of these issues simultaneously. We develop a Bayesian varying coefficient mixed-effects joint model with skewness and missingness to study the simultaneous influence of these features. The proposed methods are applied to an AIDS clinical data. Simulation studies are conducted to assess the performance of the method.