Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters

"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 t...

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Autores Principales: Tovar J.R., Achcar J.A.
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: 2012
Materias:
Acceso en línea:https://repository.urosario.edu.co/handle/10336/23012
id ir-10336-23012
recordtype dspace
spelling ir-10336-230122021-01-21T08:25:07Z Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters Dos pruebas para diagnóstico clínico: Uso de funciones copula en la estimación de la prevalencia y los parámetros de desempeño de las pruebas Tovar J.R. Achcar J.A. Bayes analysis Copula Dependence Monte carlo simulation Public health "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." 2012 2020-05-25T23:59:15Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 1201751 https://repository.urosario.edu.co/handle/10336/23012 eng info:eu-repo/semantics/openAccess application/pdf instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR
institution EdocUR - Universidad del Rosario
collection DSpace
language Inglés (English)
topic Bayes analysis
Copula
Dependence
Monte carlo simulation
Public health
spellingShingle Bayes analysis
Copula
Dependence
Monte carlo simulation
Public health
Tovar J.R.
Achcar J.A.
Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
description "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."
format Artículo (Article)
author Tovar J.R.
Achcar J.A.
author_facet Tovar J.R.
Achcar J.A.
author_sort Tovar J.R.
title Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
title_short Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
title_full Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
title_fullStr Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
title_full_unstemmed Two dependent diagnostic tests: Use of copula functions in the estimation of the prevalence and performance test parameters
title_sort two dependent diagnostic tests: use of copula functions in the estimation of the prevalence and performance test parameters
publishDate 2012
url https://repository.urosario.edu.co/handle/10336/23012
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score 11,828437