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Joint Confidence Region Estimation for Area under ROC Curve and Youden Index
Statistics in Medicine (2013)
  • Jingjing Yin, Georgia Southern University
  • Lili Tian, State University of New York at Buffalo

In the field of diagnostic studies, the area under the ROC curve (AUC) serves as an overall measure of a biomarker/diagnostic test's accuracy. Youden index, defined as the overall correct classification rate minus one at the optimal cut-off point, is another popular index. For continuous biomarkers of binary disease status, although researchers mainly evaluate the diagnostic accuracy using AUC, for the purpose of making diagnosis, Youden index provides an important and direct measure of the diagnostic accuracy at the optimal threshold and hence should be taken into consideration in addition to AUC. Furthermore, AUC and Youden index are generally correlated. In this paper, we initiate the idea of evaluating diagnostic accuracy based on AUC and Youden index simultaneously. As the first step toward this direction, this paper only focuses on the confidence region estimation of AUC and Youden index for a single marker. We present both parametric and non-parametric approaches for estimating joint confidence region of AUC and Youden index. We carry out extensive simulation study to evaluate the performance of the proposed methods. In the end, we apply the proposed methods to a real data set.

  • ROC analysis,
  • Confidence region,
  • Asymptotic delta method,
  • Generalized inference,
  • Box-Cox transformation,
  • Bootstrap method
Publication Date
Citation Information
Jingjing Yin and Lili Tian. "Joint Confidence Region Estimation for Area under ROC Curve and Youden Index" Statistics in Medicine Vol. 33 Iss. 6 (2013)
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