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Analyzing a Portion of the ROC Curve

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  • Donna Katzman McClish

Abstract

The area under the ROC curve is a common index summarizing the information contained in the curve. When comparing two ROC curves, though, problems arise when interest does not lie in the entire range of false-positive rates (and hence the entire area). Numerical integration is suggested for evaluating the area under a portion of the ROC curve. Variance estimates are derived. The method is applicable for either continuous or rating scale binormal data, from independent or dependent samples. An example is presented which looks at rating scale data of computed tomographic scans of the head with and without concomitant use of clinical history. The areas under the two ROC curves over an a priori range of false- positive rates are examined, as well as the areas under the two curves at a specific point.

Suggested Citation

  • Donna Katzman McClish, 1989. "Analyzing a Portion of the ROC Curve," Medical Decision Making, , vol. 9(3), pages 190-195, August.
    • Handle: RePEc:sae:medema:v:9:y:1989:i:3:p:190-195
    DOI: 10.1177/0272989X8900900307
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    Cited by:

    1. Gigliarano, Chiara & Figini, Silvia & Muliere, Pietro, 2014. "Making classifier performance comparisons when ROC curves intersect," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 300-312.
    2. Hand, David J., 2009. "Mining the past to determine the future: Problems and possibilities," International Journal of Forecasting, Elsevier, vol. 25(3), pages 441-451, July.
    3. Yousef, Waleed A., 2013. "Assessing classifiers in terms of the partial area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 51-70.
    4. Hajiseyedjavadi, Seyedsaeed & Karimi, Hassan A. & Blackhurst, Michael, 2022. "Predicting lead water service lateral locations: Geospatial data science in support of municipal programming," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    5. Yu, Wenbao & Park, Taesung, 2015. "Two simple algorithms on linear combination of multiple biomarkers to maximize partial area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 15-27.
    6. Schmid Matthias & Hothorn Torsten & Krause Friedemann & Rabe Christina, 2012. "A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-26, October.
    7. Ma, Hua & Bandos, Andriy I. & Gur, David, 2018. "Informativeness of diagnostic marker values and the impact of data grouping," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 76-89.
    8. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).

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