Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling
Preventive Veterinary Medicine, ISSN: 0167-5877, Vol: 68, Issue: 2, Page: 145-163
2005
- 458Citations
- 397Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations458
- Citation Indexes451
- 451
- CrossRef428
- Policy Citations7
- Policy Citation7
- Captures397
- Readers397
- 345
- 52
- Mentions1
- News Mentions1
- News1
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Article Description
We review recent Bayesian approaches to estimation (based on cross-sectional sampling designs) of the sensitivity and specificity of one or more diagnostic tests. Our primary goal is to provide veterinary researchers with a concise presentation of the computational aspects involved in using the Bayesian framework for test evaluation. We consider estimation of diagnostic-test sensitivity and specificity in the following settings: (i) one test in one population, (ii) two conditionally independent tests in two or more populations, (iii) two correlated tests in two or more populations, and (iv) three tests in two or more populations, where two tests are correlated but jointly independent of the third test. For each scenario, we describe a Bayesian model that incorporates parameters of interest. The WinBUGS code used to fit each model, which is available at http://www.epi.ucdavis.edu/diagnostictests/, can be altered readily to conform to different data.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0167587705000334; http://www.epi.ucdavis.edu/diagnostictests/; http://dx.doi.org/10.1016/j.prevetmed.2004.12.005; http://www.sciencedirect.com/science/article/pii/S0167587705001674; http://dx.doi.org/10.1016/j.prevetmed.2005.04.004; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=9644264494&origin=inward; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=23944448555&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/15820113; http://www.ncbi.nlm.nih.gov/pubmed/16076507; https://linkinghub.elsevier.com/retrieve/pii/S0167587705001674; https://linkinghub.elsevier.com/retrieve/pii/S0167587705000334; https://dx.doi.org/10.1016/j.prevetmed.2005.04.004; https://dx.doi.org/10.1016/j.prevetmed.2004.12.005
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