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ROC Curve Analysis in the Presence of Imperfect Reference Standards

Author

Listed:
  • Peizhou Liao

    (Emory University)

  • Hao Wu

    (Emory University)

  • Tianwei Yu

    (Emory University)

Abstract

The receiver operating characteristic (ROC) curve is an important tool for the evaluation and comparison of predictive models when the outcome is binary. If the class membership of the outcomes is known, ROC can be constructed for a model, and the ROC with greater area under the curve indicates better performance. However in practice, imperfect reference standards often exist, in which class membership of every data point is not fully determined. This situation is especially prevalent in high-throughput biomedical data because obtaining perfect reference standards for all data points is either too costly or technically impractical. To construct ROC curves for these data, the common practice is to either ignore the uncertainties in references or remove data points with high uncertainties. Such approaches may cause bias to the ROC curves and generate misleading results in method evaluation. Here we present a framework to incorporate membership uncertainties into the construction of ROC curve, termed the expected ROC or “eROC” curve. We develop an efficient procedure for the estimation of eROC curve. The advantages of using eROC are demonstrated using simulated and real data.

Suggested Citation

  • Peizhou Liao & Hao Wu & Tianwei Yu, 2017. "ROC Curve Analysis in the Presence of Imperfect Reference Standards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 91-104, June.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9159-7
    DOI: 10.1007/s12561-016-9159-7
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    References listed on IDEAS

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    1. Hong, Yili, 2013. "On computing the distribution function for the Poisson binomial distribution," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 41-51.
    2. Paul S. Albert & Aiyi Liu & Tonja Nansel, 2014. "Efficient logistic regression designs under an imperfect population identifier," Biometrics, The International Biometric Society, vol. 70(1), pages 175-184, March.
    3. Xiao-Hua Zhou & Pete Castelluccio & Chuan Zhou, 2005. "Nonparametric Estimation of ROC Curves in the Absence of a Gold Standard," Biometrics, The International Biometric Society, vol. 61(2), pages 600-609, June.
    4. Tianwei Yu, 2012. "ROCS: Receiver Operating Characteristic Surface for Class-Skewed High-Throughput Data," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-8, July.
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