Abstract
Bayesian mixture models, often termed latent class models, allow users to estimate the diagnostic accuracy of tests and true prevalence in one or more populations when the positive and/or negative reference standards are imperfect. Moreover, they allow the data analyst to show the superiority of a novel test over an old test, even if this old test is the (imperfect) reference standard. We use published data on Toxoplasmosis in pigs to explore the effects of numbers of tests, numbers of populations, and dependence structure among tests to ensure model (local) identifiability. We discuss and make recommendations about use of priors, sensitivity analysis, model identifiability and study design options, and strongly argue for the use of Bayesian mixture models as a logical and coherent approach for estimating the diagnostic accuracy of two or more tests.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 113-127 |
| Number of pages | 15 |
| Journal | Preventive Veterinary Medicine |
| Volume | 167 |
| DOIs | |
| State | Published - Jun 1 2019 |
| Externally published | Yes |
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Keywords
- Bayesian
- Diagnostic sensitivity and specificity
- Mixture models
- Toxoplasmosis
ASJC Scopus subject areas
- Food Animals
- Animal Science and Zoology
Cite this
Gold standards are out and Bayes is in : Implementing the cure for imperfect reference tests in diagnostic accuracy studies. / Johnson, Wesley O.; Jones, Geoff; Gardner, Ian.
In: Preventive Veterinary Medicine, Vol. 167, 01.06.2019, p. 113-127.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Gold standards are out and Bayes is in
T2 - Implementing the cure for imperfect reference tests in diagnostic accuracy studies
AU - Johnson, Wesley O.
AU - Jones, Geoff
AU - Gardner, Ian
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Bayesian mixture models, often termed latent class models, allow users to estimate the diagnostic accuracy of tests and true prevalence in one or more populations when the positive and/or negative reference standards are imperfect. Moreover, they allow the data analyst to show the superiority of a novel test over an old test, even if this old test is the (imperfect) reference standard. We use published data on Toxoplasmosis in pigs to explore the effects of numbers of tests, numbers of populations, and dependence structure among tests to ensure model (local) identifiability. We discuss and make recommendations about use of priors, sensitivity analysis, model identifiability and study design options, and strongly argue for the use of Bayesian mixture models as a logical and coherent approach for estimating the diagnostic accuracy of two or more tests.
AB - Bayesian mixture models, often termed latent class models, allow users to estimate the diagnostic accuracy of tests and true prevalence in one or more populations when the positive and/or negative reference standards are imperfect. Moreover, they allow the data analyst to show the superiority of a novel test over an old test, even if this old test is the (imperfect) reference standard. We use published data on Toxoplasmosis in pigs to explore the effects of numbers of tests, numbers of populations, and dependence structure among tests to ensure model (local) identifiability. We discuss and make recommendations about use of priors, sensitivity analysis, model identifiability and study design options, and strongly argue for the use of Bayesian mixture models as a logical and coherent approach for estimating the diagnostic accuracy of two or more tests.
KW - Bayesian
KW - Diagnostic sensitivity and specificity
KW - Mixture models
KW - Toxoplasmosis
UR - http://www.scopus.com/inward/record.url?scp=85064150294&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064150294&partnerID=8YFLogxK
U2 - 10.1016/j.prevetmed.2019.01.010
DO - 10.1016/j.prevetmed.2019.01.010
M3 - Article
C2 - 31027713
AN - SCOPUS:85064150294
VL - 167
SP - 113
EP - 127
JO - Preventive Veterinary Medicine
JF - Preventive Veterinary Medicine
SN - 0167-5877
ER -