Robust Joint Modeling of Event Time and Longitudinal Data with Skewness and Measurement Errors in Covariates

• Activity Number: 26
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yangxin Huang*+ and Getachew Dagne and Joan Hu
• Companies: University of South Florida and University of South Florida and Simon Fraser University
• Keywords: Bayesian analysis ; Covariate measurement errors ; Longitudinal data ; Semiparametric mixed-effects joint models ; Skew-elliptical distributions ; Time-to-event

Abstract

Normality of model random errors is a routine assumption for mixed-effects models in many longitudinal studies, but it may be unrealistically obscuring important features of subject variations. Covariates are usually introduced in the models to partially explain inter-subject variations, but some covariates may be measured with substantial errors. In many practical situations, the time-to-event of interest is likely related to the longitudinal response and covariate processes. This paper investigates a class of Bayesian semiparametric NLME joint models with skew-elliptical distributions which incorporates three components: response and covariate longitudinal processes in which model errors are skew-elliptical distributions including skew-normal and skew-t distributions, and an accelerated failure time process with an unspecified distribution that has the Dirichlet process prior. We illus