The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC

TL;DR

The paper introduces the R package JMbayes, which implements Bayesian joint modeling of longitudinal and time-to-event data using MCMC, enabling flexible modeling, dynamic predictions, and validation tools.

Contribution

It presents the capabilities of the JMbayes package for fitting a wide range of joint models with Bayesian methods, including flexible association structures and prediction validation.

Findings

Successfully applied to real patient data with primary biliary cirrhosis.

Provides tools for dynamic prediction and model validation.

Supports various types of longitudinal responses.

Abstract

Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markon chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.