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Overestimation of the receiver operating characteristic curve for logistic regression

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  • J. B. Copas
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    Abstract

    Logistic regression is often used to find a linear combination of covariates which best discriminates between two groups or populations. The ROC, receiver operating characteristic, curve is a good way of assessing the performance of the resulting score, but using the same data both to fit the score and to calculate its ROC leads to an over-optimistic estimate of the performance which the score would give if it were to be validated on a sample of future cases. The paper studies the extent of this overestimation, and suggests a shrinkage correction for the ROC curve itself and for the area under the curve. The correction is consistent with Efron's formula for the bias in the error rate of a binary prediction rule. Two medical examples are discussed. Copyright Biometrika Trust 2002, Oxford University Press.

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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 89 (2002)
    Issue (Month): 2 (June)
    Pages: 315-331

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    Handle: RePEc:oup:biomet:v:89:y:2002:i:2:p:315-331

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    Cited by:
    1. Margaret Pepe & Tianxi Cai & Zheng Zhang, 2004. "Combining Predictors for Classification Using the Area Under the ROC Curve," UW Biostatistics Working Paper Series 1021, Berkeley Electronic Press.

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