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Estimating the area under a receiver operating characteristic curve using partially ordered sets.

Ehsan Zamanzade1, Xinlei Wang2

  • 1Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan, 81746-73441, Iran.

The International Journal of Biostatistics
|August 9, 2020
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Summary

Ranked set sampling with ties (RSS-t) improves the efficiency of estimating the area under the ROC curve (AUC). Utilizing tie information in diagnostic marker evaluation enhances AUC estimation accuracy.

Keywords:
imperfect rankingisotonic estimationmaximum likelihoodnonparametric estimationrelative efficiencytie information

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Diagnostics

Background:

  • Ranked set sampling (RSS) is a cost-effective method requiring complete unit ranking.
  • A modification, RSS-t, allows for ties, offering greater flexibility.
  • Area under the ROC curve (AUC) is a standard metric for diagnostic marker effectiveness.

Purpose of the Study:

  • To develop and compare nonparametric estimators for AUC using RSS-t samples.
  • To evaluate the impact of utilizing tie information on AUC estimation efficiency.

Main Methods:

  • Development of six nonparametric AUC estimators for RSS-t data.
  • Comparison of estimators with and without tie information.
  • Monte Carlo simulations and analysis of real-world data (NHANES).

Main Results:

  • Estimators utilizing tie information demonstrated increased efficiency in AUC estimation.
  • Empirical studies confirmed the benefits of incorporating tie data.
  • Performance varied across different estimators and data characteristics.

Conclusions:

  • Utilizing tie information within the RSS-t framework significantly enhances AUC estimation efficiency.
  • The study provides guidance for practitioners on selecting appropriate AUC estimators.
  • RSS-t offers a flexible and efficient approach for diagnostic marker evaluation.