Abstract
Sensitivity and specificity are used to characterize the accuracy of a diagnostic test. Receiver operating characteristic (ROC) analysis can be used more generally to plot the sensitivity versus (1-specificity) overall possible cutoff points. We develop an ROC analysis that can be applied to diagnostic tests with and without a gold standard. Moreover, the method can be applied to multiple correlated diagnostic tests that are used on the same individual. Simulation studies were performed to assess the discrimination ability of the no-gold-standard method compared with the situation where a gold standard exists. We used the area under the ROC curve (AUC) to quantify the diagnostic accuracy of tests and the difference between AUCs to compare their accuracies. In particular, we can estimate the prevalence of disease/infection under the no-gold-standard method. The method we proposed works well in the absence of a gold standard for correlated test data. Correlation affected the width of posterior probability intervals for these differences. The proposed method was used to analyze ELISA test scores for Johne's disease in dairy cattle.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 210-229 |
| Number of pages | 20 |
| Journal | Journal of Agricultural, Biological, and Environmental Statistics |
| Volume | 11 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2006 |
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Keywords
- Diagnostic test
- Markov chain Monte Carlo
- Sensitivity
- Serology
- 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
Bayesian inferences for receiver operating characteristic curves in the absence of a gold standard. / Choi, Young Ku; Johnson, Wesley O.; Collins, Michael T.; Gardner, Ian.
In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 11, No. 2, 01.06.2006, p. 210-229.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bayesian inferences for receiver operating characteristic curves in the absence of a gold standard
AU - Choi, Young Ku
AU - Johnson, Wesley O.
AU - Collins, Michael T.
AU - Gardner, Ian
PY - 2006/6/1
Y1 - 2006/6/1
N2 - Sensitivity and specificity are used to characterize the accuracy of a diagnostic test. Receiver operating characteristic (ROC) analysis can be used more generally to plot the sensitivity versus (1-specificity) overall possible cutoff points. We develop an ROC analysis that can be applied to diagnostic tests with and without a gold standard. Moreover, the method can be applied to multiple correlated diagnostic tests that are used on the same individual. Simulation studies were performed to assess the discrimination ability of the no-gold-standard method compared with the situation where a gold standard exists. We used the area under the ROC curve (AUC) to quantify the diagnostic accuracy of tests and the difference between AUCs to compare their accuracies. In particular, we can estimate the prevalence of disease/infection under the no-gold-standard method. The method we proposed works well in the absence of a gold standard for correlated test data. Correlation affected the width of posterior probability intervals for these differences. The proposed method was used to analyze ELISA test scores for Johne's disease in dairy cattle.
AB - Sensitivity and specificity are used to characterize the accuracy of a diagnostic test. Receiver operating characteristic (ROC) analysis can be used more generally to plot the sensitivity versus (1-specificity) overall possible cutoff points. We develop an ROC analysis that can be applied to diagnostic tests with and without a gold standard. Moreover, the method can be applied to multiple correlated diagnostic tests that are used on the same individual. Simulation studies were performed to assess the discrimination ability of the no-gold-standard method compared with the situation where a gold standard exists. We used the area under the ROC curve (AUC) to quantify the diagnostic accuracy of tests and the difference between AUCs to compare their accuracies. In particular, we can estimate the prevalence of disease/infection under the no-gold-standard method. The method we proposed works well in the absence of a gold standard for correlated test data. Correlation affected the width of posterior probability intervals for these differences. The proposed method was used to analyze ELISA test scores for Johne's disease in dairy cattle.
KW - Diagnostic test
KW - Markov chain Monte Carlo
KW - Sensitivity
KW - Serology
KW - Specificity
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UR - http://www.scopus.com/inward/citedby.url?scp=33744792696&partnerID=8YFLogxK
U2 - 10.1198/108571106X110883
DO - 10.1198/108571106X110883
M3 - Article
AN - SCOPUS:33744792696
VL - 11
SP - 210
EP - 229
JO - Journal of Agricultural, Biological, and Environmental Statistics
JF - Journal of Agricultural, Biological, and Environmental Statistics
SN - 1085-7117
IS - 2
ER -