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

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.