title: Semiparametric Bayesian inference on skew-normal joint modeling of multivariate longitudinal and survival data.
abstract: We propose a semiparametric multivariate skew-normal joint model for multivariate longitudinal and multivariate survival data. One main feature of the posited model is that we relax the commonly used normality assumption for random effects and within-subject error by using a centered Dirichlet process prior to specify the random effects distribution and using a multivariate skew-normal distribution to specify the within-subject error distribution and model trajectory functions of longitudinal responses semiparametrically. A Bayesian approach is proposed to simultaneously obtain Bayesian estimates of unknown parameters, random effects and nonparametric functions by combining the Gibbs sampler and the Metropolis-Hastings algorithm. Particularly, a Bayesian local influence approach is developed to assess the effect of minor perturbations to within-subject measurement error and random effects. Several simulation studies and an example are presented to illustrate the proposed methodologies.
authors.affiliation: Department of Statistics, Yunnan University, Kunming, Yunnan, 650091, China.
authors.forename: An-Min
authors.initials: AM
authors.lastname: Tang
authors.forename: Nian-Sheng
authors.initials: NS
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods; D001943:Breast Neoplasms / Q000401:mortality / Q000523:psychology; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D011788:Quality of Life; D016019:Survival Analysis
keywords: Bayesian local influence analysis; centered Dirichlet process prior; joint models; skew-normal distribution; survival data