Bayesian joint modeling for assessing the progression of chronic kidney disease in children. (January 2018)

• Record Type: Journal Article
• Authors: Armero, Carmen; Forte, Anabel; Perpiñán, Hèctor; Sanahuja, María José; Agustí, Silvia
• Is Part Of: Statistical methods in medical research. Volume 27:Number 1(2018)
• Page Start: 298
• Page End: 311
• Publication Date: 2018-01
• Subjects: Competing risks -- left truncation -- longitudinal data -- non-ignorable dropout -- random-effect joint models; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216628560
• Languages: English
• ISSNs: 0962-2802

Abstract

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.