Reflection on modern methods: Dynamic prediction using joint models of longitudinal and time-to-event data

Eleni Rosalina Andrinopoulou*, Michael O. Harhay, Sarah J. Ratcliffe, Dimitris Rizopoulos

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)
28 Downloads (Pure)


Individualized prediction is a hallmark of clinical medicine and decision making. However, most existing prediction models rely on biomarkers and clinical outcomes available at a single time. This is in contrast to how health states progress and how physicians deliver care, which relies on progressively updating a prognosis based on available information. With the use of joint models of longitudinal and survival data, it is possible to dynamically adjust individual predictions regarding patient prognosis. This article aims to introduce the reader to the development of dynamic risk predictions and to provide the necessary resources to support their implementation and assessment, such as adaptable R code, and the theory behind the methodology. Furthermore, measures to assess the predictive performance of the derived predictions and extensions that could improve the predictions are presented. We illustrate personalized predictions using an online dataset consisting of patients with chronic liver disease (primary biliary cirrhosis).

Original languageEnglish
Pages (from-to)1731-1743
Number of pages13
JournalInternational Journal of Epidemiology
Issue number5
Early online date17 Mar 2021
Publication statusPublished - Oct 2021

Bibliographical note

Funding Information:
M.O.H. is partially supported by grants R00-HL141678 and R01-DK123041 from the United States (US) National Institutes of Health (NIH). S.J.R. is partially supported by grants R01-GM104470 and R01-DK123041 from the US NIH. E.R.A. is partially supported by the NIH/National Heart, Lung, and Blood Institute (grant R01 HL141286)

Publisher Copyright:
© 2021 The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.


Dive into the research topics of 'Reflection on modern methods: Dynamic prediction using joint models of longitudinal and time-to-event data'. Together they form a unique fingerprint.

Cite this