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dc.contributor.authorMorel, Maryan
dc.contributor.authorBacry, Emmanuel
dc.contributor.authorGaïffas, Stéphane
dc.contributor.authorGuilloux, Agathe
dc.contributor.authorLeroy, Fanny
dc.date.accessioned2019-12-16T12:47:25Z
dc.date.available2019-12-16T12:47:25Z
dc.date.issued2019
dc.identifier.issn1465-4644
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20325
dc.language.isoenen
dc.subjectConditional Poisson modelen
dc.subjectPenalizationen
dc.subjectRisk screeningen
dc.subjectScalabilityen
dc.subjectSelf-controlled case seriesen
dc.subjectTotal variationen
dc.subject.ddc519en
dc.titleConvSCCS: convolutional self-controlled case-seris model for lagged adverser event detectionen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWith the increased availability of large electronic health records databases comes the chance of enhancing health risks screening. Most post-marketing detection of adverse drug reaction (ADR) relies on physicians' spontaneous reports, leading to under-reporting. To take up this challenge, we develop a scalable model to estimate the effect of multiple longitudinal features (drug exposures) on a rare longitudinal outcome. Our procedure is based on a conditional Poisson regression model also known as self-controlled case series (SCCS). To overcome the need of precise risk periods specification, we model the intensity of outcomes using a convolution between exposures and step functions, which are penalized using a combination of group-Lasso and total-variation. Up to our knowledge, this is the first SCCS model with flexible intensity able to handle multiple longitudinal features in a single model. We show that this approach improves the state-of-the-art in terms of mean absolute error and computation time for the estimation of relative risks on simulated data. We apply this method on an ADR detection problem, using a cohort of diabetic patients extracted from the large French national health insurance database (SNIIRAM), a claims database containing medical reimbursements of more than 53 million people. This work has been done in the context of a research partnership between Ecole Polytechnique and CNAMTS (in charge of SNIIRAM).en
dc.relation.isversionofjnlnameBiostatistics
dc.relation.isversionofjnldate2019-03
dc.relation.isversionofdoi10.1093/biostatistics/kxz003en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2019-12-16T10:38:01Z
hal.person.labIds89626
hal.person.labIds60
hal.person.labIds102
hal.person.labIds89626
hal.person.labIds162303
hal.identifierhal-02413920*


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