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ROC Curves and the Areas under Them for Dichotomized Tests

Author

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  • Yvonne T. Van Der Schouw
  • Huub Straatman
  • Andre L.M. Verbeek

Abstract

Many measures, including sensitivity and specificity, predictive values, and likelihood ratios, are available for the assessment of diagnostic tests. A drawback of the use of these measures is that continuous test results are often dichotomized, with consequent loss of information. Receiver operating characteristic (ROC) curves do not depend on discrimination thresholds, and therefore the area under the ROC curve (AUC) is one of the preferred measures. Although quantitative test results are often presented dichotomized, it would be convenient still to be able to estimate the ROC curve and the AUC. The authors present equations for such estimates when only one pair of a true- and a false-positive rate is given, for inherently logistically and normally distributed data. Illustrative empirical data are provided for both distributions. In contradiction to earlier reports, the authors also show that differential disease verification may skew the ROC curve. The ROC curve is thus not invariant to selection bias. Key words: diagnosis; test assessment; ROC curves; verification bias; sensitivity; specificity; colorectal carcinoma; acute myocardial infarction. (Med Decis Making 1994;14:374-381)

Suggested Citation

  • Yvonne T. Van Der Schouw & Huub Straatman & Andre L.M. Verbeek, 1994. "ROC Curves and the Areas under Them for Dichotomized Tests," Medical Decision Making, , vol. 14(4), pages 374-381, October.
    • Handle: RePEc:sae:medema:v:14:y:1994:i:4:p:374-381
    DOI: 10.1177/0272989X9401400408
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    Cited by:

    1. Wilbert B. van den Hout, 2003. "The Area under an ROC Curve with Limited Information," Medical Decision Making, , vol. 23(2), pages 160-166, March.

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