{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T13:17:56Z","timestamp":1793020676603,"version":"4.5.0"},"reference-count":51,"publisher":"Wiley","issue":"13-14","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"French National Research Agency","doi-asserted-by":"publisher","award":["ANR\u201021\u2010CE36"],"award-info":[{"award-number":["ANR\u201021\u2010CE36"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models including many longitudinal markers is still a computational challenge because of the high number of random effects and parameters to be estimated. In this paper, we propose a model averaging strategy to combine predictions from several joint models for the event, including one longitudinal marker only or pairwise longitudinal markers. The prediction is computed as the weighted mean of the predictions from the one\u2010marker or two\u2010marker models, with the time\u2010dependent weights estimated by minimizing the time\u2010dependent Brier score. This method enables us to combine a large number of predictions issued from joint models to achieve a reliable and accurate individual prediction. Advantages and limits of the proposed methods are highlighted in a simulation study by comparison with the predictions from well\u2010specified and misspecified all\u2010marker joint models, as well as the one\u2010marker and two\u2010marker joint models. Using the PBC2 data set, the method is used to predict the risk of death in patients with primary biliary cirrhosis. The method is also used to analyze a French cohort study called the 3C data. In our study, seventeen longitudinal markers are considered to predict the risk of death.<\/jats:p>","DOI":"10.1002\/sim.70122","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T01:15:12Z","timestamp":1748999712000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach"],"prefix":"10.1002","volume":"44","author":[{"given":"Reza","family":"Hashemi","sequence":"first","affiliation":[{"name":"Department of Statistics Razi University  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2100-4532","authenticated-orcid":false,"given":"Taban","family":"Baghfalaki","sequence":"additional","affiliation":[{"name":"Department of Mathematics The University of Manchester  Manchester UK"},{"name":"Inserm, Research Center U1219 Univ. 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