Bayesian inference on longitudinal-survival data with multiple features

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2 Scopus citations


The modeling of longitudinal and survival data is an active research area. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a joint model that concurrently accounting for longitudinal-survival data with multiple features. Specifically, our joint model handles skewness, limit of detection, missingness and measurement errors in covariates which are typical observed in the collection of longitudinal-survival data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. A few alternative models under different conditions are compared. Some interesting results are reported. Simulation studies are conducted to assess the performance of the proposed methods.

Original languageEnglish
Pages (from-to)845-866
Number of pages22
JournalComputational Statistics
Issue number3
StatePublished - Sep 1 2017


  • AIDS study
  • Longitudinal data
  • Measurement error
  • Missing data
  • Proportional hazard models
  • Semiparametric mixed-effects models
  • Skew distribution
  • Survival data


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