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In clinical settings, dynamic survival prediction is particularly important to refine prognosis by incorporating patient information updated over time. Landmarking methods have emerged as a flexible approach for this purpose, as they allow to summarize longitudinal covariates up to a given landmark time and use these summaries in subsequent prediction. For mixture cure models, the only landmarking strategy available in the literature relies on summarizing the dynamics of observed longitudinal markers through the last observation carried forward (LOCF) method. However, LOCF discards most of the longitudinal information, does not correct for measurement error, and may rely on outdated values if observation times are far apart. To overcome these limitations, we propose a sequential approach that integrates model\u2010based landmarking within a mixture cure model. Initially, longitudinal covariates are modeled using (generalized) linear mixed models, from which individual\u2010specific summaries are predicted. The predicted summaries are then incorporated as covariates into a Cox proportional hazards cure model. We investigated the performance of the proposed approach under different cure fractions, sample sizes, and longitudinal data structures through an extensive simulation study. The results show that the model\u2010based strategy provides more refined predictions compared to LOCF, even when the model is misspecified in favor of the LOCF approach. Finally, we illustrate our method using a real\u2010world dataset on renal transplant patients.<\/jats:p>","DOI":"10.1002\/sim.70774","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:29:30Z","timestamp":1792384170000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Prediction in Mixture Cure Models: A Model\u2010Based Landmarking Approach"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6159-8059","authenticated-orcid":false,"given":"Marta","family":"Cipriani","sequence":"first","affiliation":[{"name":"Department of Statistical Sciences Sapienza University of Rome  Rome Italy"},{"name":"School of Mathematics and Statistics University College Dublin  Dublin Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-6052","authenticated-orcid":false,"given":"Marco","family":"Alf\u00f2","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences Sapienza University of Rome  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-3356","authenticated-orcid":false,"given":"Mirko","family":"Signorelli","sequence":"additional","affiliation":[{"name":"Mathematical Institute Leiden University  Leiden the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3294-8"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.2307\/2533118"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35588-2_19"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9469.2006.00529.x"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8334"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/09622802231224631"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280220941532"},{"key":"e_1_2_10_9_1","unstructured":"A.Devaux C.Proust\u2010Lima andR.Geneur \u201cRandom Forests for Time\u2010Fixed and Time\u2010Dependent Predictors: The DynForest R Package \u201d(2023) arXiv:230202670 [statML]."},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9178"},{"issue":"2","key":"e_1_2_10_11_1","first-page":"134","article-title":"Pencal: An R Package for the Dynamic Prediction of Survival With Many Longitudinal Predictors","volume":"16","author":"Signorelli M.","year":"2025","journal-title":"R Journal"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-031017-100101"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1949.tb00020.x"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1952.10501187"},{"key":"e_1_2_10_15_1","first-page":"269","volume-title":"Series in Mathematical Biology and Medicine","author":"Yakovlev A. 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