6533b838fe1ef96bd12a45f1
RESEARCH PRODUCT
Bayesian joint modeling for assessing the progression of chronic kidney disease in children.
María José SanahujaAnabel ForteCarmen ArmeroSilvia AgustíHèctor PerpiñánHèctor Perpiñánsubject
Statistics and ProbabilityEpidemiologyComputer scienceBayesian probability030232 urology & nephrologyRenal function01 natural sciences010104 statistics & probability03 medical and health sciences0302 clinical medicineHealth Information ManagementStatisticsEconometricsmedicineHumans0101 mathematicsRenal Insufficiency ChronicChildJoint (geology)Dropout (neural networks)Survival analysisAutocorrelationBayes Theoremmedicine.diseaseMissing dataSurvival AnalysisChild PreschoolDisease ProgressionKidney diseasedescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-03-19 | Statistical methods in medical research |