Beta Regression for Modeling a Covariate Adjusted ROC
Science Journal of Applied Mathematics and Statistics
Volume 6, Issue 4, August 2018, Pages: 110-118
Received: Jul. 24, 2018; Accepted: Aug. 9, 2018; Published: Sep. 11, 2018
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Sarah Stanley, Department of Statistical Science, Baylor University, Waco, USA
Jack Tubbs, Department of Statistical Science, Baylor University, Waco, USA
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Background: Several regression methodologies have been developed to model the ROC as a function of covariate effects within the generalized linear model (GLM) framework. In this article, we present an alternative to two existing parametric and semi-parametric methods for estimating a covariate adjusted ROC. The existing methods utilize GLMs for binary data when the expected value equals the probability that the test result for a diseased subject exceeds that of a non-diseased subject with the same covariate values. This probability is referred to as the placement value. Objective: The new method directly models the placement values through beta regression. Methods: We compare the proposed method to the existing models with simulation and a clinical study. Conclusion: The proposed method performs favorably with the commonly used parametric method and has better performance than the semi-parametric method when modeling the covariate adjusted ROC regression.
Placement Values, Beta Regression, ROC Regression
To cite this article
Sarah Stanley, Jack Tubbs, Beta Regression for Modeling a Covariate Adjusted ROC, Science Journal of Applied Mathematics and Statistics. Vol. 6, No. 4, 2018, pp. 110-118. doi: 10.11648/j.sjams.20180604.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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