Research Article Open Access

Application of a Beta Regression Model for Covariate Adjusted ROC

Xing Meng1 and J.D. Tubbs1
  • 1 Department of Statistical Science, Baylor University, Waco, United States

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.

Current Research in Biostatistics
Volume 10 No. 1, 2020, 20-24

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

Submitted On: 13 February 2020 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

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Keywords

  • Placement Values
  • Beta Regression
  • ROC Regression