Computational methods for comparing two binary diagnostic tests in the presence of partial verification of disease
type: Item
instance of: scholarly article
title: Computational methods for comparing two binary diagnostic tests in the presence of partial verification of disease
zbMATH Open document ID: 1233.62186
author: J. A. Roldán Nofuentes
author: P. Femia Marzo
author: Juan de Dios Luna del Castillo
published in: Computational Statistics
publication date: 2011-09-08T00:00:00Z
review text: The authors consider data of application of two binary diagnostic tests of some disease to a set (sample) of patients. The true status of the patients is known for only a subsample. The problem is to estimate the sensitivity and specificity of the tests. The Vacek model of conditional dependence is assumed for the tests results where the dependence is modeled as a multiplicative factor. A EM-type algorithm is proposed for the calculation of the maximum likelihood estimates and a SEM algorithm is described for the estimation of the covariances. Results of simulations and application to the diagnostic of coronary stenosis are presented.
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 62N03
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 5946111
zbMATH Keywords: specificity
zbMATH Keywords: sensitivity
zbMATH Keywords: SEM-algorithm
zbMATH Keywords: maximum likelihood estimates
MaRDI profile type: Publication
full work available at URL: s00180-009-0155-y
OpenAlex ID: W2041330120
Wikidata QID: Q60534528
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DOI: 10.1007/S00180-009-0155-Y
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description: scientific article; zbMATH DE number 5946111