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Two-way partial AUC and its properties.

Hanfang Yang1, Kun Lu2, Xiang Lyu3

  • 11 School of Statistics, Renmin University of China, China.

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|July 15, 2017
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Summary
This summary is machine-generated.

This study introduces a new metric, two-way partial area under the ROC curve (pAUC), for evaluating diagnostic tests. It offers direct control over both true positive and false positive rates, improving upon existing methods.

Keywords:
ROC curvediagnostic testdiscrimination capabilityfalse positive ratetrue positive rate

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Area of Science:

  • Medical Diagnostics
  • Biostatistics
  • Machine Learning Evaluation

Background:

  • Accurate performance evaluation of diagnostic tests is crucial.
  • Existing methods like FPR pAUC offer limited control over true positive rates.
  • This limitation can be misleading in practical applications.

Purpose of the Study:

  • To propose a novel performance measure, two-way pAUC, for diagnostic tests.
  • To provide a nonparametric estimator and statistical testing framework for two-way pAUC.
  • To develop methods for assessing covariate effects on two-way pAUC.

Main Methods:

  • A nonparametric estimator for two-way pAUC was developed.
  • A bootstrap-assisted testing method was established for comparing two-way pAUC.
  • A regression analysis framework was constructed to evaluate covariate effects.

Main Results:

  • The proposed two-way pAUC offers direct control over both true positive and false positive rates.
  • Simulation studies and analysis of Wisconsin Breast Cancer Data demonstrated the method's advantages.
  • Asymptotic normality for the proposed methods was established.

Conclusions:

  • The two-way pAUC is a more intuitive and reliable performance measure for diagnostic tests.
  • The developed methods and R package (tpAUC) provide practical tools for researchers.
  • This advancement enhances the evaluation of diagnostic test performance.