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Related Experiment Videos

Multireader, multicase receiver operating characteristic methodology: a bootstrap analysis

D D Dofrman1, K S Berbaum, R V Lenth

  • 1Department of Psychology, University of Iowa, Iowa City 52242, USA.

Academic Radiology
|July 1, 1995
PubMed
Summary
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Bootstrapping methods, including case-resampling and reader-case resampling, validate the Dorfman-Berbaum-Metz (DBM) and Toledano-Gatsonis (TG) receiver operating characteristic (ROC) methods. These approaches offer insights into precision and power trade-offs in reader studies.

Area of Science:

  • Medical Imaging Analysis
  • Statistical Methods in Research

Background:

  • Receiver operating characteristic (ROC) analysis is crucial for evaluating diagnostic test performance.
  • The Dorfman-Berbaum-Metz (DBM) and Toledano-Gatsonis (TG) methods are established statistical techniques for analyzing ROC data.
  • Bootstrapping offers a robust approach for validating statistical conclusions.

Purpose of the Study:

  • To evaluate the validity of the DBM and TG methods using bootstrapping techniques.
  • To compare conclusions derived from bootstrapping with those from DBM and TG methods.
  • To investigate the impact of different bootstrapping strategies on the reliability of ROC analysis.

Main Methods:

  • Two bootstrapping methods were employed: resampling cases with fixed readers, and resampling both cases and readers.

Related Experiment Videos

  • The study utilized a well-known dataset for comparative analysis.
  • Analysis of variance (ANOVA) of pseudovalues was used to assess reader variance and treatment interactions.

    • Main Results:

    • Case-resampling bootstrap yielded highly similar results to the DBM and TG methods for individual and average reader performance.
    • When reader variance and random interactions were negligible, bootstrap and DBM/TG methods converged.
    • Both bootstrapping methods indicated smaller standard errors for group means compared to individual reader means, suggesting a precision-power trade-off.

    Conclusions:

    • Case-resampling bootstrap provides statistical justification for the DBM and TG methods in ROC analysis.
    • The findings support the reliability of DBM and TG methods when reader variance is minimal.
    • Evidence suggests a trade-off between readers and cases impacting precision and power in the analyzed dataset.