Research Article Open Access

Application of a Beta Regression Model for Covariate Adjusted ROC

Xing Meng1 and J.D. Tubbs1
  • 1 Baylor University, United States
Current Research in Biostatistics
Volume 10 No. 1, 2020, 20-24

DOI: https://doi.org/10.3844/amjbsp.2020.20.24

Published On: 18 June 2020

How to Cite: Meng, X. & Tubbs, J. (2020). Application of a Beta Regression Model for Covariate Adjusted ROC. Current Research in Biostatistics, 10(1), 20-24. https://doi.org/10.3844/amjbsp.2020.20.24

Abstract

The Receiver Operating Characteristic (ROC) curve and the area under the ROC (AUC) are widely used in determining the diagnostic capability of a binary classification procedure. Since the test performance is affected by covariates, the ROC and AUC have been utilized in a Generalized Linear Regression (GLM) setting. In this study, we revisit a problem where the AUC regression model was used in a clinical study with discrete covariates by considering ROC regression models with both discrete and continuous covariates. The two ROC regression models are based upon a widely used parametric model and a recently published model based upon fitting the placement values with the beta distribution. The two methods are illustrated using data from a clinic study.

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Keywords

  • Placement Values
  • Beta Regression
  • ROC Regression