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Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model

dc.rights.licenseopenen_US
dc.contributor.authorEMURA, T.
dc.contributor.authorNAKATOCHI, M.
dc.contributor.authorMATSUI, S.
dc.contributor.authorMICHIMAE, H.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRONDEAU, Virginie
dc.date.accessioned2020-11-09T13:44:55Z
dc.date.available2020-11-09T13:44:55Z
dc.date.issued2018-09
dc.identifier.issn0962-2802en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/12160
dc.description.abstractEnDeveloping a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.
dc.language.isoENen_US
dc.subject.enBiostatistics
dc.title.enPersonalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model
dc.title.alternativeStat Methods Med Resen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1177/0962280216688032en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed28090814en_US
bordeaux.journalStatistical methods in medical researchen_US
bordeaux.page2842-2858en_US
bordeaux.volume27en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue9en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-02996097
hal.version1
hal.date.transferred2020-11-09T13:45:00Z
hal.exporttrue
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