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Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial — arXiv:2609.15701

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15701
License (as archived): CC0-1.0
Archived: 2026-10-16T00:36:53.479Z
SHA-256 of the archived text: f971bdaf113c0c5ce9d667632d5e6cc67d6527b183337aa044425a4579fd6e89
Stable URL of this page: https://metraipolis.com/documento/dc_eb72dee871ff

How to cite

Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial — arXiv:2609.15701 — arXiv (en), https://arxiv.org/abs/2609.15701 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb72dee871ff (Metraipolis archive dc_eb72dee871ff, sha256 f971bdaf113c0c5ce9d667632d5e6cc67d6527b183337aa044425a4579fd6e89)

BibTeX

@misc{dceb72dee871ff,
  title = {Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial — arXiv:2609.15701},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15701},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb72dee871ff; sha256 f971bdaf113c0c5ce9d667632d5e6cc67d6527b183337aa044425a4579fd6e89},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15701. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-15 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2609.15701 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T00:16:03.223Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).