Bayesian joint modeling for assessing the progression of chronic kidney disease in children

Stat Methods Med Res. 2018 Jan;27(1):298-311. doi: 10.1177/0962280216628560. Epub 2016 Mar 16.


Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

Keywords: Competing risks; left truncation; longitudinal data; non-ignorable dropout; random-effect joint models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem*
  • Child
  • Child, Preschool
  • Disease Progression*
  • Humans
  • Renal Insufficiency, Chronic / pathology*
  • Survival Analysis