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Hypothesis testing methods for multi-reader multi-case studies

Hypothesis testing methods for multi-reader multi-case studies Rationale and Objectives: Multi-reader multi-case (MRMC) studies are widely used to compare the diagnostic accuracies of different imaging modalities. Although Dorfman–Berbaum–Metz (DBM) method is the most popular one among the MRMC methods, the adaptions of ANOVA statistic for linear mixed model (LMM) are not based on solid theory and the assumption of ANOVA that all groups have the same number of samples might not be met in some situations. The purpose of the article is to investigate whether the statistics for testing fixed effect in linear mixed model can yield a closer type I error rate to nominal level. Materials and Methods: We proposed to use the statistics such as likelihood ratio test (LRT) and Wald statistic to test the hypothesis of equivalence in several imaging modalities. Extensive simulations were conducted and the application to a clinical example dataset was illustrated. Results: The simulation results showed the type I error rates of Wald statistic were closer to the nominal level under many simulated situations, especially when the simulated data was ordinal and the number of diseased and non-diseased were 100. Conclusion: The Wald statistic whose degrees of freedom ware approximated by Satterthwaite's method showed competitive performance, indicating the potential of the statistic applied in DBM model for MRMC analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Hypothesis testing methods for multi-reader multi-case studies

Hypothesis testing methods for multi-reader multi-case studies

Abstract

Rationale and Objectives: Multi-reader multi-case (MRMC) studies are widely used to compare the diagnostic accuracies of different imaging modalities. Although Dorfman–Berbaum–Metz (DBM) method is the most popular one among the MRMC methods, the adaptions of ANOVA statistic for linear mixed model (LMM) are not based on solid theory and the assumption of ANOVA that all groups have the same number of samples might not be met in some situations. The purpose of the article is to...
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Publisher
Taylor & Francis
Copyright
© 2022 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2022.2075603
Publisher site
See Article on Publisher Site

Abstract

Rationale and Objectives: Multi-reader multi-case (MRMC) studies are widely used to compare the diagnostic accuracies of different imaging modalities. Although Dorfman–Berbaum–Metz (DBM) method is the most popular one among the MRMC methods, the adaptions of ANOVA statistic for linear mixed model (LMM) are not based on solid theory and the assumption of ANOVA that all groups have the same number of samples might not be met in some situations. The purpose of the article is to investigate whether the statistics for testing fixed effect in linear mixed model can yield a closer type I error rate to nominal level. Materials and Methods: We proposed to use the statistics such as likelihood ratio test (LRT) and Wald statistic to test the hypothesis of equivalence in several imaging modalities. Extensive simulations were conducted and the application to a clinical example dataset was illustrated. Results: The simulation results showed the type I error rates of Wald statistic were closer to the nominal level under many simulated situations, especially when the simulated data was ordinal and the number of diseased and non-diseased were 100. Conclusion: The Wald statistic whose degrees of freedom ware approximated by Satterthwaite's method showed competitive performance, indicating the potential of the statistic applied in DBM model for MRMC analysis.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jun 24, 2022

Keywords: MRMC; linear mixed model; hypothesis testing method

References