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Weighted volume under the three-way receiver operating characteristic surface.

Lei Huang1, Jialiang Li2

  • 1Southwest Jiaotong University, School of Mathematics, Department of Statistics, Chengdu, China.

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|November 21, 2018
PubMed
Summary

This study introduces the weighted volume under the surface (WVUS) for evaluating medical tests with three categories. WVUS enhances diagnostic accuracy assessment by weighting specific correct classification probabilities.

Keywords:
Diagnostic medicineROC curveclassification accuracyempirical processmultiple category learningvolume under ROC surface

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Area of Science:

  • Statistics
  • Medical Diagnostics
  • Biomarker Evaluation

Background:

  • Medical tests often need to classify subjects into multiple categories.
  • Statistical measures are used to assess the performance of these diagnostic tests.
  • Three-way ROC analysis evaluates diagnostic accuracy for three-category tests.

Purpose of the Study:

  • To generalize the volume under the surface in three-way ROC analysis.
  • To introduce the weighted volume under the surface (WVUS) for enhanced diagnostic accuracy assessment.
  • To enable conditional assurance of high correct classification probabilities for specific classes.

Main Methods:

  • Studied the geometry of the ROC surface for three-category tests.
  • Generalized ROC surface volume to weighted volume under the surface (WVUS) using a weight function.
  • Developed nonparametric and parametric estimators for WVUS.
  • Investigated asymptotic properties of the estimators for statistical inference.

Main Results:

  • The proposed WVUS method allows for tailored diagnostic accuracy evaluation.
  • Asymptotic properties of WVUS estimators are provided, supporting statistical inference.
  • Simulations demonstrate the utility and necessity of WVUS.
  • A real-world liver cancer dataset illustrates the methodology's application.

Conclusions:

  • WVUS offers a flexible and powerful tool for assessing diagnostic accuracy in multi-category medical tests.
  • The method provides researchers with a way to prioritize specific classification outcomes.
  • WVUS analysis, supported by its estimators, can enhance biomarker evaluation and clinical decision-making.