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dc.rights.licenseopenen_US
dc.contributor.authorKHOURI, Charles
dc.contributor.authorNGUYEN, Thuy
dc.contributor.authorREVOL, Bruno
dc.contributor.authorLEPELLEY, Marion
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPARIENTE, Antoine
IDREF: 13395711X
dc.contributor.authorROUSTIT, Matthieu
dc.contributor.authorCRACOWSKI, Jean-Luc
dc.date.accessioned2021-08-20T08:16:57Z
dc.date.available2021-08-20T08:16:57Z
dc.date.issued2021-05-28
dc.identifier.issn1663-9812 (Print) 1663-9812en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/110172
dc.description.abstractEnBackground: A plethora of methods and models of disproportionality analyses for safety surveillance have been developed to date without consensus nor a gold standard, leading to methodological heterogeneity and substantial variability in results. We hypothesized that this variability is inversely correlated to the robustness of a signal of disproportionate reporting (SDR) and could be used to improve signal detection performances. Methods: We used a validated reference set containing 399 true and false drug-event pairs and performed, with a frequentist and a Bayesian disproportionality method, seven types of analyses (model) for which the results were very unlikely to be related to actual differences in absolute risks of ADR. We calculated sensitivity, specificity and plotted ROC curves for each model. We then evaluated the predictive capacities of all models and assessed the impact of combining such models with the number of positive SDR for a given drug-event pair through binomial regression models. Results: We found considerable variability in disproportionality analysis results, both positive and negative SDR could be generated for 60% of all drug-event pairs depending on the model used whatever their truthfulness. Furthermore, using the number of positive SDR for a given drug-event pair largely improved the signal detection performances of all models. Conclusion: We therefore advocate for the pre-registration of protocols and the presentation of a set of secondary and sensitivity analyses instead of a unique result to avoid selective outcome reporting and because variability in the results may reflect the likelihood of a signal being a true adverse drug reaction.
dc.description.sponsorshipMIAI @ Grenoble Alpes - ANR-19-P3IA-0003en_US
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.title.enLeveraging the Variability of Pharmacovigilance Disproportionality Analyses to Improve Signal Detection Performances
dc.typeArticle de revueen_US
dc.identifier.doi10.3389/fphar.2021.668765en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed34122089en_US
bordeaux.journalFrontiers in Pharmacologyen_US
bordeaux.page668765en_US
bordeaux.volume12en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamPharmacoEpi-Drugsen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDUniversité Grenoble Alpesen_US
hal.identifierhal-03323000
hal.version1
hal.date.transferred2021-08-20T08:17:01Z
hal.exporttrue
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