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A Marginal Model Approach for Analysis of Multi-reader Multi-test Receiver Operating Characteristic (ROC) Data

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  • Xiao Song

    (University of Washington)

  • Xiao-Hua Zhou

    (University of Washington)

Abstract

The receiver operating characteristic (ROC) curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests is to have each patient examined by multiple readers with multiple tests; this design is most commonly used in a radiology setting, where the results of diagnostic tests depend on a radiologist's subjective interpretation. The most widely used approach for analyzing data from such a study is the Dorfman-Berbaum-Metz (DBM) method (Dorfman, Berbaum and Metz, 1992) which utilizes a standard analysis of variance (ANOVA) model for the jackknife pseudovalues of the AUCs. Although the DBM method performs well in previous simulation studies, there is no clear theoretical basis for this approach. In this paper, focusing on continuous outcomes, we investigate the theoretical basis of this approach. Our result indicates that the DBM method does not satisfy the regular assumptions for standard ANOVA models, and thus might lead to erroneous inference. We then propose a marginal model approach based on the AUCs which can adjust for covariates as well. We derive consistent and asymptotically normal estimators for the regression coe±cients. We compare our approach with the DBM method via simulation and by an application to data from a breast cancer study. The simulation results show that both our new method and the DBM method perform well when the accuracy of tests under the study is the same and that our new method outperforms the DBM method when the accuracy of tests is not the same. The marginal model approach can be easily extended to ordinal outcomes.

Suggested Citation

  • Xiao Song & Xiao-Hua Zhou, 2004. "A Marginal Model Approach for Analysis of Multi-reader Multi-test Receiver Operating Characteristic (ROC) Data," UW Biostatistics Working Paper Series 1067, Berkeley Electronic Press.
    • Handle: RePEc:bep:uwabio:1067
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    References listed on IDEAS

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    1. Lori E. Dodd & Margaret S. Pepe, 2003. "Partial AUC Estimation and Regression," Biometrics, The International Biometric Society, vol. 59(3), pages 614-623, September.
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