Simultaneous inference for semiparametric mixed-effects joint models with skew distribution and covariate measurement error for longitudinal competing risks data analysis

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Abstract

Semiparametric mixed-effects joint models are flexible for modeling complex longitudinal–competing risks data. Skew distributions are commonly observed for this type of data. Covariates in the joint models are usually measured with substantial errors. We propose a Bayesian method for semiparametric mixed-effects joint models with covariate measurement errors and skew distribution. The methods are illustrated with AIDS clinical data. Simulation results are conducted to validate the proposed methods.

Original languageEnglish
Pages (from-to)1009-1027
Number of pages19
JournalJournal of Biopharmaceutical Statistics
Volume27
Issue number6
DOIs
StatePublished - Nov 2 2017

Keywords

  • Bayesian inference
  • competing risks
  • longitudinal data
  • measurement error
  • partially linear mixed-effects models
  • proportional hazard models
  • skew distribution
  • survival data

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