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A probabilistic model for the MRMC method, part 2: validation and applications.

Matthew A Kupinski1, Eric Clarkson, Harrison H Barrett

  • 1College of Optical Sciences, The University of Arizona, 1630 East University Blvd., Tucson, AZ 85721, USA. kupinski@radiology.arizona.edu

Academic Radiology
|October 31, 2006
PubMed
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This study validates a probabilistic model for receiver operating characteristic analysis using the Wilcoxon statistic. A novel bootstrapping and fitting technique accurately estimates variance expansion coefficients, confirming the model's utility.

Area of Science:

  • Statistical modeling
  • Medical image analysis
  • Receiver Operating Characteristic (ROC) analysis

Background:

  • A probabilistic model for the multiple-reader, multiple-case paradigm in ROC analysis was previously established.
  • This model yields a seven-term expansion for the Wilcoxon statistic's variance, dependent on case and reader numbers.

Purpose of the Study:

  • To validate the probabilistic model by comparing direct variance computations with empirical estimates.
  • To develop and assess a novel coefficient-estimation technique using bootstrapping and constrained least-squares fitting.

Main Methods:

  • Direct computation of expansion coefficients and empirical variance estimation via independent sampling.
  • Development of a bootstrapping technique for estimating Wilcoxon statistic variance.

Related Experiment Videos

  • Application of constrained, least-squares fitting using model-derived constraints.

    • Main Results:

    • Simulation studies demonstrated that the bootstrapping/fitting technique yields coefficient estimates consistent with a gold standard.
    • The novel technique proved practical and effective for estimating coefficients.

    Conclusions:

    • The study successfully validated the probabilistic model for Wilcoxon statistic variance.
    • The developed bootstrapping and fitting methodology is a reliable approach for coefficient estimation in ROC analysis.