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Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point

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  • Lai, Chin-Ying
  • Tian, Lili
  • Schisterman, Enrique F.

Abstract

In 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).

Suggested Citation

  • Lai, Chin-Ying & Tian, Lili & Schisterman, Enrique F., 2012. "Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1103-1114.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1103-1114
    DOI: 10.1016/j.csda.2010.11.023
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. K. Krishnamoorthy & Yong Lu, 2003. "Inferences on the Common Mean of Several Normal Populations Based on the Generalized Variable Method," Biometrics, The International Biometric Society, vol. 59(2), pages 237-247, June.
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    Citations

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    Cited by:

    1. Poon, Wai-Yin & Qiu, Shi-Fang & Tang, Man-Lai, 2015. "Confidence interval construction for the Youden index based on partially validated series," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 116-134.
    2. Wang, Dongliang & Tian, Lili & Zhao, Yichuan, 2017. "Smoothed empirical likelihood for the Youden index," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 1-10.
    3. Yin, Jingjing & Tian, Lili, 2014. "Joint inference about sensitivity and specificity at the optimal cut-off point associated with Youden index," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 1-13.
    4. Qiu, Zhiping & Peng, Limin & Manatunga, Amita & Guo, Ying, 2019. "A smooth nonparametric approach to determining cut-points of a continuous scale," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 186-210.
    5. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.
    6. 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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