Afficher la notice abrégée

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCHAUVET, Jocelyn
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRONDEAU, Virginie
ORCID: 0000-0001-7109-4831
IDREF: 16662988X
dc.date.accessioned2023-03-14T14:15:14Z
dc.date.available2023-03-14T14:15:14Z
dc.date.issued2023-04-15
dc.identifier.issn0277-6715en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172304
dc.description.abstractEnThis article focuses on shared frailty models for correlated failure times, as well as joint frailty models for the simultaneous analysis of recurrent events (eg, appearance of new cancerous lesions or hospital readmissions) and a major terminal event (typically, death). As extensions of the Cox model, these joint models usually assume a frailty proportional hazards model for each of the recurrent and terminal event processes. In order to extend these models beyond the proportional hazards assumption, our proposal is to replace these proportional hazards models with generalized survival models, for which the survival function is modeled as a linear predictor through a link function. Depending on the link function considered, these can be reduced to proportional hazards, proportional odds, additive hazards, or probit models. We first consider a fully parametric framework for the time and covariate effects. For proportional and additive hazards models, our approach also allows the use of smooth functions for baseline hazard functions and time-varying coefficients. The dependence between recurrent and terminal event processes is modeled by conditioning on a shared frailty acting differently on the two processes. Parameter estimates are provided using the maximum (penalized) likelihood method, implemented in the R package frailtypack (function GenfrailtyPenal). We perform simulation studies to assess the method, which is also illustrated on real datasets.
dc.language.isoENen_US
dc.subject.enFrailty models
dc.subject.enGeneralized survival models
dc.subject.enJoint modeling
dc.subject.enRecurrent events
dc.subject.enTerminal event
dc.title.enA flexible class of generalized joint frailty models for the analysis of survival endpoints
dc.title.alternativeStat Meden_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1002/sim.9667en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed36775273en_US
bordeaux.journalStatistics in Medicineen_US
bordeaux.page1233-1262en_US
bordeaux.volume42en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue8en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDInstitut National Du Canceren_US
hal.identifierhal-04028726
hal.version1
hal.date.transferred2023-03-14T14:15:17Z
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=Statistics%20in%20Medicine&rft.date=2023-04-15&rft.volume=42&rft.issue=8&rft.spage=1233-1262&rft.epage=1233-1262&rft.eissn=0277-6715&rft.issn=0277-6715&rft.au=CHAUVET,%20Jocelyn&RONDEAU,%20Virginie&rft.genre=article


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée