Semiparametric Bayesian joint models of multivariate longitudinal and survival data

Nian-Sheng Tang, An-Min Tang and Dong-Dong Pan

Computational Statistics & Data Analysis, 2014, vol. 77, issue C, 113-129

Abstract: Joint models for longitudinal and survival data are often used to investigate the association between longitudinal data and survival data in many studies. A common assumption for joint models is that random effects are distributed as a fully parametric distribution such as multivariate normal distribution. The fully parametric distribution assumption of random effects is relaxed by specifying a centered Dirichlet Process Mixture Model (CDPMM) for a general distribution of random effects because of some good properties of CDPMM such as inducing zero mean and continuous probability distribution of random effects. A computationally feasible Bayesian case-deletion diagnostic based on the ϕ-divergence is proposed to identify the potential influential cases in the joint models. Several simulation studies and a real example are used to illustrate our proposed methodologies.

Keywords: Bayesian case-deletion diagnostic; Centered Dirichlet process prior; Joint models; Longitudinal data; Semiparametric Bayesian analysis; Survival data (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:77:y:2014:i:c:p:113-129

DOI: 10.1016/j.csda.2014.02.015

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