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A comparative study of methods for testing the equality of two or more ROC curves

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  • Arís Fanjul-Hevia

    (Universidade de Santiago de Compostela)

  • Wenceslao González-Manteiga

    (Universidade de Santiago de Compostela)

Abstract

The problem of comparing the accuracy of diagnostic tests is usually carried out through the comparison of the corresponding receiver operating characteristic (ROC) curves. This matter has been approached from different perspectives. Usually, ROC curves are compared through their respective areas under the curve, but in cases where there is no uniform dominance between the involved curves other procedures are preferred. Although the asymptotic distributions of the statistics behind these methods are, in general, known, resampling plans are considered. With the purpose of comparing the performance of different approaches, with different ways of calibrating the distribution of the tests, a simulation study is carried out in order to investigate the statistical power and the nominal level of each methodology.

Suggested Citation

  • Arís Fanjul-Hevia & Wenceslao González-Manteiga, 2018. "A comparative study of methods for testing the equality of two or more ROC curves," Computational Statistics, Springer, vol. 33(1), pages 357-377, March.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0783-6
    DOI: 10.1007/s00180-017-0783-6
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    References listed on IDEAS

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    1. E. S. Venkatraman, 2000. "A Permutation Test to Compare Receiver Operating Characteristic Curves," Biometrics, The International Biometric Society, vol. 56(4), pages 1134-1138, December.
    2. Pablo Mart�nez-Camblor & Carlos Carleos & Norberto Corral, 2011. "Powerful nonparametric statistics to compare k independent ROC curves," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1317-1332, May.
    3. Lloyd, Chris J. & Yong, Zhou, 1999. "Kernel estimators of the ROC curve are better than empirical," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 221-228, September.
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    Cited by:

    1. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

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