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Related Experiment Videos

Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver

S V Beiden1, R F Wagner, G Campbell

  • 1Office of Science and Technology, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Rockville, MD 20857, USA.

Academic Radiology
|May 10, 2000
PubMed
Summary

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This study introduces a novel multiple-bootstrap method for receiver operating characteristic (ROC) analysis, offering improved variance structure estimation compared to the Dorfman, Berbaum, and Metz (DBM) approach for diagnostic modalities.

Area of Science:

  • Medical Imaging and Diagnostics
  • Statistical Modeling
  • Biostatistics

Background:

  • Receiver operating characteristic (ROC) analysis is crucial for evaluating diagnostic test performance.
  • Existing methods like the Dorfman, Berbaum, and Metz (DBM) approach have limitations in capturing full variance structure.
  • Components-of-variance models offer a framework for deeper analysis.

Purpose of the Study:

  • To develop an alternative to random-effects ROC analysis using components-of-variance models.
  • To create a higher-order generalization of the DBM approach.
  • To provide enhanced information on the variance structure in ROC analysis.

Main Methods:

  • Developed a multiple-bootstrap technique as an alternative to population experiments for estimating variance components.

Related Experiment Videos

  • Utilized Monte Carlo simulations to compare bootstrap estimates with DBM and population results.
  • Estimated confidence intervals for ROC parameters to compare diagnostic modalities.

    • Main Results:

    • The bootstrap method demonstrated excellent agreement with population variance structures.
    • Confidence intervals for ROC area differences were comparable to or tighter than the DBM method, especially with high reader variability.
    • The new approach provided estimates of variance structures not available with DBM.

    Conclusions:

    • The multiple-bootstrap method offers a more general, nonparametric, maximum-likelihood approach to ROC analysis.
    • This technique overcomes the limitations of the DBM method's linear approximation.
    • The study successfully yielded previously unavailable estimates of variance structure.