Bayesian Joint Modelling

Uses

How it works

Preparation

Comparisons

Highlighted claims

• The study used Bayesian joint modelling to connect serum creatinine trajectories with time to first AKI, CKD, or death in a UK paediatric autoimmune cohort. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
• The selected primary model used cumulative creatinine exposure as its association structure because it had the best WAIC and LPML. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
• Joint models simultaneously model repeated biomarker trajectories and event risk while accounting for measurement error, correlation, censoring, late entry, and the biomarker-event link. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
• Model estimation was performed with JMbayes2 in R using weakly informative priors and MCMC. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
• The article presents joint modelling as suitable for paediatric nephrology because it can link biomarker history and event risk while updating predictions as new measurements arrive. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study