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Components-of-variance models for random-effects ROC analysis: the case of unequal variance structures across

S V Beiden1, R F Wagner, G Campbell

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

Academic Radiology
|July 14, 2001
PubMed
Summary
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This study generalizes receiver operating characteristic (ROC) analysis for diagnostic modalities by removing the equal variance assumption. The new method provides consistent results, even when comparing modalities with different variance structures.

Area of Science:

  • Biostatistics
  • Medical Imaging Analysis
  • Diagnostic Accuracy Studies

Background:

  • Previous receiver operating characteristic (ROC) analysis for diagnostic modalities assumed equal variance components.
  • This limitation restricted comparisons between modalities with differing uncertainty structures.

Purpose of the Study:

  • To generalize multivariate ROC analysis by removing the assumption of equal variance across compared modalities.
  • To develop a method applicable to diagnostic accuracy studies with varying uncertainty.

Main Methods:

  • Extended a previous variance components model by splitting three components into modality-dependent contributions.
  • Explored two formulations of the generalized model, demonstrating a one-to-one relationship between their component estimates.

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Main Results:

  • Applied the generalized method to a reader study comparing computer-aided diagnosis (CAD) versus no CAD for microcalcification classification.
  • Observed significant reductions in reader and reader-by-case variance with CAD using one formulation.
  • Identified substantial decreases in modality-interaction components with CAD using the alternative formulation.

Conclusions:

  • Successfully provided a multivariate ROC analysis solution that does not require equal variance structures across modalities.
  • Confirmed consistent results across alternative formulations, linked by a one-to-one mapping.
  • Found that confidence intervals and sample size estimations remain consistent between the generalized and restricted models.