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Determining a Range of False-positive Rates for Which ROC Curves Differ

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  • Donna Katzman McClish

Abstract

Many indices have been proposed for summarizing the information contained in the ROC curve. When comparing two ROC curves, though, there are times when global summary measures are either not optimal or not appropriate. The author presents a method for directly comparing true-positive rates for two diagnostic, screening or prognostic tools, determining over what range of false-positive values the tests differ. The method is applicable for in dependent or dependent samples. An example concerning gallium citrate imaging is pre sented, as well as an example using a prognostic index for severity of illness in the ICU. The range of false-positive rates for which the ROC curves differ is determined for each example.

Suggested Citation

  • Donna Katzman McClish, 1990. "Determining a Range of False-positive Rates for Which ROC Curves Differ," Medical Decision Making, , vol. 10(4), pages 283-287, December.
    • Handle: RePEc:sae:medema:v:10:y:1990:i:4:p:283-287
    DOI: 10.1177/0272989X9001000406
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    Cited by:

    1. Zhang, Biao, 2006. "A semiparametric hypothesis testing procedure for the ROC curve area under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1855-1876, April.

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