Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial
Abstract
Shared-parameter joint models for longitudinal and time-to-event data are powerful tools for analyzing repeatedly measured biomarkers, clinical events, and the complex relationships between them. Recent methodological advances have extended the basic framework, which was originally developed for a single event time and a continuous longitudinal biomarker, to more complex scenarios. This tutorial provides a step-by-step guide to fitting extended joint models for longitudinal and time-to-event data using the R package JMbayes2. We cover a range of applications, including models with competing risks, recurrent events, multistate processes, flexible association structures, and multiple longitudinal outcomes following different distributions. Each model is illustrated using simulated data that closely resemble a real-world dataset, with detailed explanations of data organization, model specification, fitting, diagnostics, and interpretation. The tutorial is designed for applied researchers who are interested in analyzing their own data with extended joint models using accessible and reproducible R code.
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BibTeX
@misc{mirandaafonso2026extended,
title = {{Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial}},
author = {Pedro Miranda-Afonso and Dimitris Rizopoulos},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.15701},
eprint = {2609.15701},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.15701},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial
AU - Pedro Miranda-Afonso
AU - Dimitris Rizopoulos
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.15701
UR - https://arxiv.org/abs/2609.15701
AB - Shared-parameter joint models for longitudinal and time-to-event data are powerful tools for analyzing repeatedly measured biomarkers, clinical events, and the complex relationships between them. Recent methodological advances have extended the basic framework, which was originally developed for a single event time and a continuous longitudinal biomarker, to more complex scenarios. This tutorial provides a step-by-step guide to fitting extended joint models for longitudinal and time-to-event data using the R package JMbayes2. We cover a range of applications, including models with competing risks, recurrent events, multistate processes, flexible association structures, and multiple longitudinal outcomes following different distributions. Each model is illustrated using simulated data that closely resemble a real-world dataset, with detailed explanations of data organization, model specification, fitting, diagnostics, and interpretation. The tutorial is designed for applied researchers who are interested in analyzing their own data with extended joint models using accessible and reproducible R code.
ER -