Bayesian varying coefficient mixed-effects joint models with asymmetry and missingness

Tao Lu, Chunyan Cai, Minggen Lu, Jun Zhang, Guang Hui Dong, Min Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Longitudinal and survival data are often collected from clinical studies. Mixed-effects joint models are commonly used for the analysis of such data. Nevertheless, the following issues may arise in longitudinal survival data analysis: (a) most joint models assume a simple parametric mixed-effects model for longitudinal outcome, which may obscure the important relationship between response and covariates; (b) clinical data often exhibits asymmetry so that symmetric assumption for model errors may lead to biased estimation of parameters; (c) response may be missing and missingness may be informative. There is little work concerning all of these issues simultaneously. We develop a Bayesian varying coefficient mixed-effects joint model with skewness and missingness to study the simultaneous influence of these features. The proposed methods are applied to an AIDS clinical data. Simulation studies are conducted to assess the performance of the method.

Original languageEnglish
Pages (from-to)117-141
Number of pages25
JournalStatistical Modelling
Volume17
Issue number3
DOIs
StatePublished - Jun 2017

Keywords

  • Bayesian inference
  • Competing risks
  • asymmetric distribution
  • longitudinal data
  • missing data
  • proportional hazard models
  • survival data
  • varying coefficient mixed-effects models

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