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dc.creatorTovar J.R. 
dc.creatorAchcar J.A. 
dc.date.accessioned2020-05-25T23:59:15Z
dc.date.available2020-05-25T23:59:15Z
dc.date.created2012
dc.identifier.issn1201751
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23012
dc.description.abstract"In this paper, we introduce a Bayesian analysis to estimate the prevalence and performance test parameters of two diagnostic tests. We concentrated our interest in studies where the individuals with negative outcomes in both tests are not verified by a gold standard. Given that the screening tests are applied in the same individual we assume dependence between test results. Generally, to capture the possible existing dependence between test outcomes, it is assumed a binary covariance structure, but in this paper, as an alternative for this modeling, we consider the use of copula function structures. The posterior summaries of interest are obtained using standard MCMC (Markov Chain Monte Carlo) methods. We compare the results obtained with our approach with those obtained using binary covariance and assuming independence. We considerate two published medical data sets to illustrate the approach."
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.relation.ispartofRevista Colombiana de Estadistica, ISSN:1201751, Vol.35, No.3 (2012); pp. 331-347
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84871630067&partnerID=40&md5=49b08947305086481b9536f21ac6ed0e
dc.sourceinstname:Universidad del Rosario
dc.sourcereponame:Repositorio Institucional EdocUR
dc.titleTwo dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
dc.typearticle
dc.subject.keywordBayes analysis
dc.subject.keywordCopula
dc.subject.keywordDependence
dc.subject.keywordMonte carlo simulation
dc.subject.keywordPublic health
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaArtículo
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.title.TranslatedTitleDos pruebas para diagnóstico clínico: Uso de funciones copula en la estimación de la prevalencia y los parámetros de desempeño de las pruebas
dc.relation.citationEndPage347
dc.relation.citationIssueNo. 3
dc.relation.citationStartPage331
dc.relation.citationTitleRevista Colombiana de Estadistica
dc.relation.citationVolumeVol. 35


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