Statistical Modelling 17 (3) (2017), 117–141

Bayesian varying coefficient mixed-effects joint models with asymmetry and missingness

Tao Lu
Department of Mathematics and Statistics,
University of Nevada,
Reno, NV,
USA
e-mail: stat.lu11@gmail.com

Chunyan Cai
Biostatistics/Epidemiology/Research Design Core,
Center for Clinical and Translational Sciences,
The University of Texas Health Science Center,
Houston, TX,
USA


Minggen Lu
School of Community Health Sciences,
University of Nevada,
Reno, NV,
USA


Jun Zhang
College of Mathematics and Statistics,
Institute of Statistical Sciences,
Shen Zhen-Hong Kong Joint Research Center for Applied Statistical Sciences,
Shenzhen University,
Shenzhen,
China


Guang-Hui Dong
Department of Preventive Medicine,
Sun Yat-sen University,
Guangzhou,
China


Min Wang
Department of Mathematical Sciences,
Michigan Technological University,
Houghton, Michigan,
USA


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.

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