{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T22:11:26Z","timestamp":1790719886908,"version":"4.1.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"United States (US) National Institutes of Health","award":["R00-HL141678"],"award-info":[{"award-number":["R00-HL141678"]}]},{"name":"United States (US) National Institutes of Health","award":["R01-DK123041"],"award-info":[{"award-number":["R01-DK123041"]}]},{"name":"United States (US) National Institutes of Health","award":["R01-GM104470"],"award-info":[{"award-number":["R01-GM104470"]}]},{"name":"United States (US) National Institutes of Health","award":["R01-DK123041"],"award-info":[{"award-number":["R01-DK123041"]}]},{"name":"US NIH"},{"DOI":"10.13039\/100000050","name":"NIH\/National Heart, Lung, and Blood Institute","doi-asserted-by":"crossref","award":["R01 HL141286"],"award-info":[{"award-number":["R01 HL141286"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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).<\/jats:p>","DOI":"10.1093\/ije\/dyab047","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T07:20:34Z","timestamp":1614324034000},"page":"1731-1743","source":"Crossref","is-referenced-by-count":53,"title":["Reflection on modern methods: Dynamic prediction using joint models of longitudinal and time-to-event data"],"prefix":"10.1093","volume":"50","author":[{"given":"Eleni-Rosalina","family":"Andrinopoulou","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0553-674X","authenticated-orcid":false,"given":"Michael O","family":"Harhay","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Pulmonary, Allergy, and Critical Care Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah J","family":"Ratcliffe","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Rizopoulos","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"2022012214114807100_dyab047-B1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s12872-015-0035-z","article-title":"Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data","volume":"15","author":"Andrinopoulou","year":"2015","journal-title":"BMC Cardiovasc Disord"},{"key":"2022012214114807100_dyab047-B2","author":"Al-Huniti","year":"2017"},{"key":"2022012214114807100_dyab047-B3","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1002\/hep.1840200120","article-title":"Primary biliary cirrhosis: prediction of short-term survival based on repeated patient visits","volume":"20","author":"Murtaugh","year":"1994","journal-title":"Hepatology"},{"key":"2022012214114807100_dyab047-B4","first-page":"809","article-title":"Joint modeling of longitudinal and time-to-event data: an overview","volume":"14","author":"Tsiatis","year":"2004","journal-title":"Statistica Sinica"},{"key":"2022012214114807100_dyab047-B5","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1093\/ije\/dyu262","article-title":"Joint modelling of repeated measurement and time-to-event data: an introductory tutorial","volume":"44","author":"Asar","year":"2015","journal-title":"Int J Epidemiol"},{"key":"2022012214114807100_dyab047-B6","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1146\/annurev-statistics-030718-105048","article-title":"An overview of joint modeling of time-to-event and longitudinal outcomes","volume":"6","author":"Papageorgiou","year":"2019","journal-title":"Annu Rev Stat Appl"},{"key":"2022012214114807100_dyab047-B7","doi-asserted-by":"crossref","first-page":"330","DOI":"10.2307\/2533118","article-title":"joint model for survival and longitudinal data measured with error","volume":"53","author":"Wulfsohn","year":"1997","journal-title":"Biometrics"},{"key":"2022012214114807100_dyab047-B8","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1093\/biostatistics\/3.1.33","article-title":"Identification and efficacy of longitudinal markers for survival","volume":"3","author":"Henderson","year":"2002","journal-title":"Biostatistics"},{"key":"2022012214114807100_dyab047-B9","doi-asserted-by":"crossref","DOI":"10.1201\/b12208","volume-title":"Joint Models for Longitudinal and Time-to-event Data: With Applications in R","author":"Rizopoulos","year":"2012"},{"key":"2022012214114807100_dyab047-B10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1186\/s12874-016-0212-5","article-title":"Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues","volume":"16","author":"Hickey","year":"2016","journal-title":"BMC Med Res Methodol"},{"key":"2022012214114807100_dyab047-B11","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1200\/JCO.2005.12.156","article-title":"Individualized predictions of disease progression following radiation therapy for prostate cancer","volume":"23","author":"Taylor","year":"2005","journal-title":"J Clin Oncol"},{"key":"2022012214114807100_dyab047-B12","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1198\/016214507000000400","article-title":"Individual prediction in prostate cancer studies using a joint longitudinal survival\u2013cure model","volume":"103","author":"Yu","year":"2008","journal-title":"J Am Stat Assoc"},{"key":"2022012214114807100_dyab047-B13","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1111\/j.1541-0420.2010.01546.x","article-title":"Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data","volume":"67","author":"Rizopoulos","year":"2011","journal-title":"Biometrics"},{"key":"2022012214114807100_dyab047-B14","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1093\/biostatistics\/kxv031","article-title":"Personalized screening intervals for biomarkers using joint models for longitudinal and survival data","volume":"17","author":"Rizopoulos","year":"2016","journal-title":"Biostatistics"},{"key":"2022012214114807100_dyab047-B15","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1177\/0962280217722177","article-title":"Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer\u2019s disease","volume":"28","author":"Li","year":"2019","journal-title":"Stat Methods Med Res"},{"key":"2022012214114807100_dyab047-B16","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for some traditional and novel measures","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"2022012214114807100_dyab047-B17","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-19425-7","volume-title":"Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis","author":"Harrell","year":"2015"},{"key":"2022012214114807100_dyab047-B18","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1002\/bimj.201600238","article-title":"Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking","volume":"59","author":"Rizopoulos","year":"2017","journal-title":"Biom J"},{"key":"2022012214114807100_dyab047-B19","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1177\/0962280215588340","article-title":"Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data","volume":"26","author":"Andrinopoulou","year":"2017","journal-title":"Stat Methods Med Res"},{"key":"2022012214114807100_dyab047-B20","doi-asserted-by":"crossref","first-page":"3649","DOI":"10.1177\/0962280218811837","article-title":"Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment","volume":"28","author":"Ferrer","year":"2019","journal-title":"Stat Methods Med Res"},{"key":"2022012214114807100_dyab047-B21","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1080\/01621459.2014.931236","article-title":"Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging","volume":"109","author":"Rizopoulos","year":"2014","journal-title":"J Am Stat Assoc"},{"key":"2022012214114807100_dyab047-B22","doi-asserted-by":"crossref","first-page":"5623","DOI":"10.1002\/sim.8387","article-title":"Individualized dynamic prediction of survival with the presence of intermediate events","volume":"38","author":"Papageorgiou","year":"2019","journal-title":"Stat Med"},{"key":"2022012214114807100_dyab047-B23","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1111\/biom.12814","article-title":"Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using p-splines","volume":"74","author":"Andrinopoulou","year":"2018","journal-title":"Biometrics"},{"key":"2022012214114807100_dyab047-B24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v072.i07","article-title":"The r package jmbayes for fitting joint models for longitudinal and time-to-event data using mcmc","volume":"72","author":"Rizopoulos","year":"2016","journal-title":"J Stat Softw"},{"key":"2022012214114807100_dyab047-B25","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s12874-019-0666-3","article-title":"A review of spline function procedures in r","volume":"19","author":"Perperoglou","year":"2019","journal-title":"BMC Med Res Methodol"},{"key":"2022012214114807100_dyab047-B26","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1002\/sim.8570","article-title":"Graphical calibration curves and the integrated calibration index (ici) for survival models","volume":"39","author":"Austin","year":"2020","journal-title":"Stat Med"},{"key":"2022012214114807100_dyab047-B27","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat Med"},{"key":"2022012214114807100_dyab047-B28","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1002\/bimj.201600235","article-title":"Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model","volume":"59","author":"Suresh","year":"2017","journal-title":"Biom J"},{"key":"2022012214114807100_dyab047-B29","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1177\/0962280212445839","article-title":"Joint latent class models for longitudinal and time-to-event data: A review","volume":"23","author":"Proust-Lima","year":"2014","journal-title":"Stat Methods Med Res"},{"key":"2022012214114807100_dyab047-B30","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1093\/biostatistics\/kxp009","article-title":"Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach","volume":"10","author":"Proust-Lima","year":"2009","journal-title":"Biostatistics"},{"key":"2022012214114807100_dyab047-B31","first-page":"1","article-title":"B. Estimation of extended mixed models using latent classes and latent processes: the r package lcmm","volume":"78","author":"Proust-Lima","year":"2015","journal-title":"J Stat Softw"}],"container-title":["International Journal of Epidemiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ije\/article-pdf\/50\/5\/1731\/42260015\/dyab047.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ije\/article-pdf\/50\/5\/1731\/42260015\/dyab047.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,22]],"date-time":"2022-01-22T09:12:23Z","timestamp":1642842743000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ije\/article\/50\/5\/1731\/6174516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,17]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3,17]]},"published-print":{"date-parts":[[2021,11,10]]}},"URL":"https:\/\/doi.org\/10.1093\/ije\/dyab047","relation":{},"ISSN":["0300-5771","1464-3685"],"issn-type":[{"value":"0300-5771","type":"print"},{"value":"1464-3685","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,10,1]]},"published":{"date-parts":[[2021,3,17]]}}}