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Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests.

Wei Liu1,2, Norberto Pantoja-Galicia2, Bo Zhang2

  • 11 Department of Mathematics, Harbin Institute of Technology, Harbin, P. R. China.

Statistical Methods in Medical Research
|April 8, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces generalized linear mixed models to analyze multi-reader multi-case (MRMC) diagnostic test data. These models improve upon the Obuchowski-Rockette method by constraining area under the curve (AUC) estimates to the unit interval for more accurate comparisons.

Keywords:
AUCEM algorithmROC curvediagnostic medicinepseudo-likelihoodrandom effect

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Area of Science:

  • Biostatistics
  • Medical Imaging Analysis
  • Diagnostic Test Evaluation

Background:

  • Multi-reader multi-case (MRMC) studies are essential for comparing diagnostic tests.
  • The Obuchowski-Rockette (OR) method is a common analysis technique for MRMC data.
  • The OR method assumes a linear mixed model for the area under the ROC curve (AUC), which may not always be appropriate.

Purpose of the Study:

  • To propose generalized linear mixed models (GLMMs) for MRMC data analysis.
  • To incorporate range-appropriate link functions within GLMMs to constrain AUCs to the unit interval.
  • To provide a statistically robust method for comparing diagnostic tests in MRMC studies.

Main Methods:

  • Development of GLMMs that generalize the OR model.
  • Utilizing range-appropriate link functions to ensure AUCs are within the [0, 1] interval.
  • Estimation via pseudo-likelihood maximization using a Monte Carlo expectation-maximization algorithm.
  • Inference through a non-parametric bootstrap procedure.

Main Results:

  • The proposed GLMMs offer a flexible framework for MRMC data analysis.
  • Simulation studies demonstrated the validity and performance of the new method.
  • The method was successfully applied to a real-world MRMC study for breast cancer detection.

Conclusions:

  • Generalized linear mixed models provide a more appropriate statistical framework for analyzing MRMC data compared to the traditional OR method.
  • The proposed method ensures valid AUC estimates and robust statistical inference.
  • This approach enhances the reliability of diagnostic test comparisons in clinical research.