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.

Bayesian case-deletion diagnostic; Centered Dirichlet process prior; Joint models; Longitudinal data; Semiparametric Bayesian analysis; Survival data

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