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Quantifying rater variation for ordinal data using a rating scale model.

Shiqi Zhang1, Jørgen Holm Petersen1

  • 1Department of Biostatistics, University of Copenhagen, Denmark.

Statistics in Medicine
|April 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical model for analyzing agreement among raters using ordinal data, particularly when no gold standard exists. The median odds ratio quantifies rater variation effectively, aiding diagnostic accuracy in fields like breast cancer screening.

Keywords:
MORbreast cancer diagnosisinter-rater variationordinal regression modelrating scale model

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Area of Science:

  • Biostatistics
  • Medical Imaging Analysis
  • Ordinal Data Modeling

Background:

  • Assessing inter-rater reliability is crucial in medical diagnosis, especially for subjective assessments like mammogram interpretation.
  • Traditional methods often require a gold standard, which is frequently unavailable in clinical practice.
  • Ordinal rating scales are commonly used but present unique challenges for agreement analysis.

Purpose of the Study:

  • To develop and present a model-based approach for analyzing agreement between multiple raters using ordinal data without a gold standard.
  • To introduce a method for quantifying rater variation on the same scale as the observed ordinal data.
  • To illustrate the model's application using a real-world example of breast cancer diagnosis from mammograms.

Main Methods:

  • Employed an ordinal regression model with random effects, specifically a rating scale model.
  • Incorporated case-specific parameters to account for individual variations in disease severity.
  • Included rater-specific parameters to capture individual differences in rating propensities.
  • Proposed the median odds ratio for quantifying inter-rater variation.

Main Results:

  • The proposed rating scale model effectively accommodates both case-specific and rater-specific effects in ordinal data.
  • The median odds ratio provides an interpretable measure of rater variation, comparable to the observed ordinal scale.
  • Demonstrated successful application in analyzing agreement among radiologists assessing breast cancer risk from mammograms.

Conclusions:

  • The model-based approach offers a robust framework for analyzing inter-rater agreement with ordinal data, even without a gold standard.
  • The median odds ratio is a valuable tool for understanding and quantifying rater variability in diagnostic settings.
  • This methodology can enhance the reliability and consistency of diagnostic assessments in medical imaging and other fields.