Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Jointly modeling longitudinal and survival data has been an active research area. Most researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In the current study, we develop the joint models that concurrently accounting for longitudinal and survival data with multiple features. Specifically, the proposed model handles skewness, missingness and measurement errors in covariates which are typically observed in the collection of longitudinal survival data from many studies. We employ a Bayesian inferential method to make inference on the proposed model. We applied the proposed model to an real data study. A few alternative models under different conditions are compared. We conduct extensive simulations in order to evaluate how the method works.

Original languageEnglish
Pages (from-to)2354-2367
Number of pages14
JournalJournal of Applied Statistics
Issue number13
StatePublished - Oct 3 2017


  • Bayesian inference
  • competing risks
  • covariate measurement errors
  • longitudinal data
  • measurement error
  • missing data
  • mixed-effects models
  • proportional hazard models
  • skew distribution
  • survival data

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