title: Bayesian joint modeling for assessing the progression of chronic kidney disease in children.
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.
authors.affiliation: 1 Department of Statistics and Operational Research, Universitat de València, Burjassot, Spain.
authors.forename: Carmen
authors.initials: C
authors.lastname: Armero
authors.affiliation: 1 Department of Statistics and Operational Research, Universitat de València, Burjassot, Spain.
authors.forename: Anabel
authors.initials: A
authors.lastname: Forte
authors.affiliation: 1 Department of Statistics and Operational Research, Universitat de València, Burjassot, Spain.|2 Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (Fisabio), Valencia, Spain.
authors.forename: Hèctor
authors.initials: H
authors.lastname: Perpiñán
authors.affiliation: 3 Conselleria de Sanitat i Consum, Generalitat Valenciana, Valencia, Spain.
authors.forename: María José
authors.initials: MJ
authors.lastname: Sanahuja
authors.affiliation: 3 Conselleria de Sanitat i Consum, Generalitat Valenciana, Valencia, Spain.
authors.forename: Silvia
authors.initials: S
authors.lastname: Agustí
mesh_terms: D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D018450:Disease Progression; D006801:Humans; D051436:Renal Insufficiency, Chronic / Q000473:pathology*; D016019:Survival Analysis
keywords: Competing risks; left truncation; longitudinal data; non-ignorable dropout; random-effect joint models