A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets

Dong D. Zhang, Xia Hua Zhou, Daniel H. Freeman, Jean L. Freeman

Research output: Contribution to journalArticle

94 Scopus citations


The receiver operating characteristic (ROC) curve is a statistical tool for evaluating the accuracy of diagnostic tests. Investigators often compare the validity of two tests based on the estimated areas under the respective ROC curves. However, the traditional way of comparing entire areas under two ROC curves is not sensitive when two ROC curves cross each other. Also, there are some cutpoints on the ROC curves that are not considered in practice because their corresponding sensitivities or specificities are unacceptable. For the purpose of comparing the partial area under the curve (AUC) within a specific range of specificity for two correlated ROC curves, a non-parametric method based on Mann-Whitney U-statistics has been developed. The estimation of AUC along with its estimated variance and covariance is simplified by a method of grouping the observations according to their cutpoint values. The method is used to evaluate alternative logistic regression models that predict whether a subject has incident breast cancer based on information in Medicare claims data.

Original languageEnglish (US)
Pages (from-to)701-715
Number of pages15
JournalStatistics in Medicine
Issue number5
StatePublished - Mar 15 2002



  • Area under ROC curve
  • Breast cancer
  • Medicare claims
  • Non-parametric method
  • Portions of two ROC curves

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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