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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDINART, Derek
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorBELLERA, Carine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRONDEAU, Virginie
ORCID: 0000-0001-7109-4831
IDREF: 16662988X
dc.date.accessioned2024-03-14T14:14:40Z
dc.date.available2024-03-14T14:14:40Z
dc.date.issued2024-02-09
dc.identifier.issn1520-5711 (Electronic) 1054-3406 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/188799
dc.description.abstractEnIn epidemiology and clinical research, recurrent events refer to individuals who are likely to experience transient clinical events repeatedly over an observation period. Examples include hospitalizations in patients with heart failure, fractures in osteoporosis studies and the occurrence of new lesions in oncology. We provided an in-depth analysis of the sample size required for the analysis of recurrent time-to-event data using multifrailty or multilevel survival models. We covered the topic from the simple shared frailty model to models with hierarchical or joint frailties. We relied on a Wald-type test statistic to estimate the sample size assuming either a single or multiple endpoints. Simulations revealed that the sample size increased as heterogeneity increased. We also observed that it was more attractive to include more patients and reduce the duration of follow-up than to include fewer patients and increase the duration of follow-up to obtain the number of events required. Each model investigated can address the question of the number of subjects for recurrent events. However, depending on the research question, one model will be more suitable than another. We illustrated our methodology with the AFFIRM-AHF trial investigating the effect of intravenous ferric carboxymaltose in patients hospitalised for acute heart failure.
dc.language.isoENen_US
dc.subject.enSample size
dc.subject.enFrailty model
dc.subject.enJoint models
dc.subject.enRandomized
dc.subject.enRecurrent events
dc.title.enSample size estimation for recurrent event data using multifrailty and multilevel survival models
dc.title.alternativeJ Biopharm Staten_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1080/10543406.2024.2310306en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed38334044en_US
bordeaux.journalJournal of Biopharmaceutical Statisticsen_US
bordeaux.page1-16en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamEPICENE_BPHen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04504934
hal.version1
hal.date.transferred2024-03-14T14:14:42Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Biopharmaceutical%20Statistics&rft.date=2024-02-09&rft.spage=1-16&rft.epage=1-16&rft.eissn=1520-5711%20(Electronic)%201054-3406%20(Linking)&rft.issn=1520-5711%20(Electronic)%201054-3406%20(Linking)&rft.au=DINART,%20Derek&BELLERA,%20Carine&RONDEAU,%20Virginie&rft.genre=article


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