Abstract
A common assumption made in studies involving two or more binary diagnostic tests in the absence of a gold standard is one of conditional independence among tests given disease status. Although reasonable in some cases, often this assumption is untenable or untested and may lead to biased results. We propose a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 223-239 |
| Number of pages | 17 |
| Journal | Journal of Agricultural, Biological, and Environmental Statistics |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2003 |
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Keywords
- Sensitivity
- Specificity
ASJC Scopus subject areas
- Statistics and Probability
- Environmental Science(all)
- Agricultural and Biological Sciences (miscellaneous)
- Agricultural and Biological Sciences(all)
- Statistics, Probability and Uncertainty
- Applied Mathematics
Cite this
Hierarchical models for estimating herd prevalence and test accuracy in the absence of a gold standard. / Hanson, Timothy; Johnson, Wesley O.; Gardner, Ian.
In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 8, No. 2, 01.06.2003, p. 223-239.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Hierarchical models for estimating herd prevalence and test accuracy in the absence of a gold standard
AU - Hanson, Timothy
AU - Johnson, Wesley O.
AU - Gardner, Ian
PY - 2003/6/1
Y1 - 2003/6/1
N2 - A common assumption made in studies involving two or more binary diagnostic tests in the absence of a gold standard is one of conditional independence among tests given disease status. Although reasonable in some cases, often this assumption is untenable or untested and may lead to biased results. We propose a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.
AB - A common assumption made in studies involving two or more binary diagnostic tests in the absence of a gold standard is one of conditional independence among tests given disease status. Although reasonable in some cases, often this assumption is untenable or untested and may lead to biased results. We propose a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.
KW - Sensitivity
KW - Specificity
UR - http://www.scopus.com/inward/record.url?scp=0141754238&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0141754238&partnerID=8YFLogxK
U2 - 10.1198/1085711031526
DO - 10.1198/1085711031526
M3 - Article
AN - SCOPUS:0141754238
VL - 8
SP - 223
EP - 239
JO - Journal of Agricultural, Biological, and Environmental Statistics
JF - Journal of Agricultural, Biological, and Environmental Statistics
SN - 1085-7117
IS - 2
ER -