Semiparametric Bayesian inference on skew-normal joint modeling of multivariate longitudinal and survival data

• Published in: Statistics in medicine Vol. 34; no. 5; pp. 824 - 843
• Main Authors: Tang, An-Min, Tang, Nian-Sheng
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 28.02.2015; Wiley Subscription Services, Inc
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Bayesian local influence analysis; Biostatistics - methods; Breast Neoplasms - mortality; Breast Neoplasms - psychology; centered Dirichlet process prior; Clinical Trials as Topic - statistics & numerical data; Computer Simulation; Female; Humans; joint models; Longitudinal Studies; Medical statistics; Models, Statistical; Multivariate Analysis; Normal distribution; Quality of Life; Simulation; skew-normal distribution; Survival Analysis; survival data; Bayesian local influence analysis; skew-normal distribution; survival data; joint models; centered Dirichlet process prior
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6373

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. Copyright © 2014 John Wiley & Sons, Ltd.