2007
DOI: 10.1016/j.vetmic.2006.10.025
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Evaluation of three serological tests for diagnosis of Maedi-Visna virus infection using latent class analysis

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Cited by 53 publications
(30 citation statements)
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“…The model was designed to determine the sensitivity and specificity of three diagnostic tests using data from two populations in the absence of a gold standard using 2 tests that are conditionally dependent (ZME and IFAT) and a third is conditionally independent (PCR). The code for the model was adapted from code provided by Nils Toft, which is published by Toft et al (2007) (See Appendix 1). Data from testing the samples with the ELISA were omitted from the final analysis because the inclusion of these data did not produce repeatable estimates of the parameters of interest regardless of test combination and model used (data not shown).…”
Section: Discussionmentioning
confidence: 99%
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“…The model was designed to determine the sensitivity and specificity of three diagnostic tests using data from two populations in the absence of a gold standard using 2 tests that are conditionally dependent (ZME and IFAT) and a third is conditionally independent (PCR). The code for the model was adapted from code provided by Nils Toft, which is published by Toft et al (2007) (See Appendix 1). Data from testing the samples with the ELISA were omitted from the final analysis because the inclusion of these data did not produce repeatable estimates of the parameters of interest regardless of test combination and model used (data not shown).…”
Section: Discussionmentioning
confidence: 99%
“…The priors for the conditional covariance for M a n u s c r i p t 11 the ZME and IFAT were modeled using uniform distributions with ranges given by the limits derived in Appendix 1 according to Toft et al (2007).…”
Section: Discussionmentioning
confidence: 99%
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“…A number of approaches have been used to overcome this in the development of welfare indicators, including, the use of a 'Pseudo Gold Standard' Bertrand et al, 2005) and Bayesian methods, such as latent class analysis (LCA; Hui and Walter, 1980). LCA has frequently been applied in the evaluation of veterinary diagnostic tests Toft et al, 2007a;Bonde et al, 2010), and in the evaluation of the diagnostic accuracy of clinical observations of animal-based outcomes of pigs (Baadsgaard and Jørgensen, 2003). Accordingly, this approach was applied in the current study.…”
Section: Introductionmentioning
confidence: 99%
“…Araştırmacılar farklı doğrulama testleri ile daha çok çalışma yapılması gerektiğini vurgulamışlardır 48 .…”
Section: Gizli Sınıf Analizinin Kullanıldığı Hayvanlarda Yapılan çAlıunclassified