Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point
AbstractIn diagnostic studies, the receiver operating characteristic (ROC) curve and the area under the ROC curve are important tools in assessing the utility of biomarkers in discriminating between non-diseased and diseased populations. For classifying a patient into the non-diseased or diseased group, an optimal cut-point of a continuous biomarker is desirable. Youden’s index (J), defined as the maximum vertical distance between the ROC curve and the diagonal line, serves as another global measure of overall diagnostic accuracy and can be used in choosing an optimal cut-point. The proposed approach is to make use of a generalized approach to estimate the confidence intervals of the Youden index and its corresponding optimal cut-point. Simulation results are provided for comparing the coverage probabilities of the confidence intervals based on the proposed method with those based on the large sample method and the parametric bootstrap method. Finally, the proposed method is illustrated via an application to a data set from a study on Duchenne muscular dystrophy (DMD).
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 5 ()
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Web page: http://www.elsevier.com/locate/csda
Confidence interval; ROC curve; Sensitivity and specificity; Youden index; Optimal cut-point; Generalized pivotal quantity;
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- Tian, Lili & Wilding, Gregory E., 2008. "Confidence interval estimation of a common correlation coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4872-4877, June.
- Gamage, Jinadasa & Mathew, Thomas & Weerahandi, Samaradasa, 2004. "Generalized p-values and generalized confidence regions for the multivariate Behrens-Fisher problem and MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 177-189, January.
- Rota, Matteo & Antolini, Laura, 2014. "Finding the optimal cut-point for Gaussian and Gamma distributed biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 1-14.
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