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Updated: Aug 13, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is
C Enøe1, M P Georgiadis, W O Johnson
1Department of Animal Science and Animal Health, Division of Ethology and Health, Royal Veterinary and Agricultural University, DK-1870, Frederiksberg C, Denmark. cle@kvl.dk
Abstract:
The performance of a new diagnostic test is frequently evaluated by comparison to a perfect reference test (i.e. a gold standard). In many instances, however, a reference test is less than perfect. In this paper, we review methods for estimation of the accuracy of a diagnostic test when an imperfect reference test with known classification errors is available. Furthermore, we focus our presentation on available methods of estimation of test characteristics when the sensitivity and specificity of both tests are unknown. We present some of the available statistical methods for estimation of the accuracy of diagnostic tests when a reference test does not exist (including maximum likelihood estimation and Bayesian inference). We illustrate the application of the described methods using data from an evaluation of a nested polymerase chain reaction and microscopic examination of kidney imprints for detection of Nucleospora salmonis in rainbow trout.
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