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Bayesian inference of dependent kappa for binary ratings.
Ananda Sen1,2, Pin Li1,3, Wen Ye1
1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Statistics in Medicine
|September 20, 2021
Summary
This study introduces novel Bayesian methods to compare dependent agreement measures in medical research. The new methods demonstrate superior power and accuracy for analyzing correlated testing outcomes across multiple methods.
Area of Science:
- Medical Statistics
- Biostatistics
- Diagnostic Accuracy Research
Background:
- Reliability of diagnostic testing, assessed via inter- and intraobserver agreement, is crucial in medical and social sciences.
- Comparing agreement across multiple testing methods is common, especially when outcomes are correlated due to repeated measures on the same subjects.
Purpose of the Study:
- To develop and evaluate Bayesian methodologies for comparing dependent agreement measures in grouped data settings.
- To introduce a Bayesian joint model that accounts for subject and rater heterogeneity when comparing agreement measures.
Main Methods:
- Development of a Bayesian method for comparing dependent agreement measures under a grouped data framework.
- Creation of a Bayesian joint model to adjust for subject and rater heterogeneity.
- Utilized simulation studies to assess the performance (power and type I error rate) of the proposed methods against competing approaches.
Main Results:
- The proposed Bayesian methodology demonstrated superior power compared to existing methods while maintaining acceptable type I error rates.
- The Bayesian joint model also outperformed a competing method in simulation studies for comparing dependent agreement measures with heterogeneity.
- The methodology was successfully applied to analyze chest radiograph classifications for pneumoconiosis.
Conclusions:
- The developed Bayesian methods provide a robust framework for comparing dependent agreement measures in complex research settings.
- These novel approaches offer improved statistical power and accuracy for diagnostic accuracy studies involving correlated data and heterogeneity.
- The application to pneumoconiosis classification highlights the practical utility of the methodology in real-world medical research.
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