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dc.rights.licenseopenen_US
dc.contributor.authorASSELINEAU, Julien
dc.contributor.authorPAYE, A.
dc.contributor.authorBESSEDE, E.
dc.contributor.authorPEREZ, P.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST-LIMA, Cecile
dc.date.accessioned2020-10-19T07:12:11Z
dc.date.available2020-10-19T07:12:11Z
dc.date.issued2018-09
dc.identifier.issn0950-2688en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/11387
dc.description.abstractEnIn the absence of perfect reference standard, classical techniques result in biased diagnostic accuracy and prevalence estimates. By statistically defining the true disease status, latent class models (LCM) constitute a promising alternative. However, LCM is a complex method which relies on parametric assumptions, including usually a conditional independence between tests and might suffer from data sparseness. We carefully applied LCMs to assess new campylobacter infection detection tests for which bacteriological culture is an imperfect reference standard. Five diagnostic tests (culture, polymerase chain reaction and three immunoenzymatic tests) of campylobacter infection were collected in 623 patients from Bordeaux and Lyon Hospitals, France. Their diagnostic accuracy were estimated with standard and extended LCMs with a thorough examination of models goodness-of-fit. The model including a residual dependence specific to the immunoenzymatic tests best complied with LCM assumptions. Asymptotic results of goodness-of-fit statistics were substantially impaired by data sparseness and empirical distributions were preferred. Results confirmed moderate sensitivity of the culture and high performances of immunoenzymatic tests. LCMs can be used to estimate diagnostic tests accuracy in the absence of perfect reference standard. However, their implementation and assessment require specific attention due to data sparseness and limitations of existing software.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enUSMR
dc.subject.enbiostatistics
dc.title.enDifferent latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?
dc.title.alternativeEpidemiol Infecten_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1017/s0950268818001723en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed29945689en_US
bordeaux.journalEpidemiology and infectionen_US
bordeaux.page1556-1564en_US
bordeaux.volume146en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue12en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamBIOSTAT_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-02970821
hal.version1
hal.date.transferred2020-10-19T07:12:16Z
hal.exporttrue
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