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dc.rights.licenseopenen_US
dc.contributor.authorCOLLONNAZ, M.
dc.contributor.authorERPELDING, M. L.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorALLA, Francois
dc.contributor.authorGOEHRINGER, F.
dc.contributor.authorDELAHAYE, F.
dc.contributor.authorIUNG, B.
dc.contributor.authorLE MOING, V.
dc.contributor.authorHOEN, B.
dc.contributor.authorSELTON-SUTY, C.
dc.contributor.authorAGRINIER, N.
dc.date.accessioned2021-03-15T09:54:10Z
dc.date.available2021-03-15T09:54:10Z
dc.date.issued2020-12
dc.identifier.issn2352-3409en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/26655
dc.description.abstractEnThis article describes supplementary tables and figures associated with the research paper entitled "Impact of referral bias on prognostic studies outcomes: insights from a population-based cohort study on infective endocarditis". The aforementioned paper is a secondary analysis of data from the EI 2008 cohort on infective endocarditis and aimed at characterising referral bias. A total of 497 patients diagnosed with definite infective endocarditis between January 1(st) and December 31(st) 2008 were included in EI 2008. Data were collected from hospital medical records by trained clinical research assistants. Patients were divided into three groups: admitted to a tertiary hospital (group T), admitted to a non-tertiary hospital and referred secondarily to a tertiary hospital (group NTT) or admitted to a non-tertiary hospital and not referred (group NT). The pooled (NTT+T) group mimicked studies recruiting patients in tertiary hospitals only. Two different starting points were considered for follow up: date of first hospital admission and date of first admission to a tertiary hospital if any (hereinafter referred to as "referral time"). Referral bias is a type of selection bias which can occur due to recruitment of patients in tertiary hospitals only (excluding those who are admitted to non-tertiary hospitals and not referred to tertiary hospitals). This bias may impact the description of patients' characteristics, survival estimates as well as prognostic factors identification. The six tables presented in this paper illustrate how patients' selection (population-based sample [pooled (NT+NTT+T) group] versus recruitment in tertiary hospitals only [pooled (NTT+T) group]) might impact Hazards Ratios values for prognostic factors. Crude and adjusted Cox regression analyses were first performed to identify prognostic factors associated with 3-month and 1-year mortality in the whole sample using inclusion as the starting point. Analyses were then performed in the pooled (NTT+T) group first using inclusion as the starting point and finally using referral time as the starting point. Figures 1 to 3 illustrate how HR increase with time for covariates that were considered as time-varying covariates (covariate*time interaction).
dc.language.isoENen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.title.enData on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis
dc.title.alternativeData Briefen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.dib.2020.106478en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed33225027en_US
bordeaux.journalData in Briefen_US
bordeaux.page106478en_US
bordeaux.volume33en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamMRISPen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03169210
hal.version1
hal.date.transferred2021-03-15T09:54:14Z
hal.exporttrue
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