@article {10.3844/amjbsp.2020.20.24,
article_type = {journal},
title = {Application of a Beta Regression Model for Covariate Adjusted ROC},
author = {Meng, Xing and Tubbs, J.D.},
volume = {10},
year = {2020},
month = {Jun},
pages = {20-24},
doi = {10.3844/amjbsp.2020.20.24},
url = {https://thescipub.com/abstract/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.},
journal = {Current Research in Biostatistics},
publisher = {Science Publications}
}