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.
Highlights
The Receiver Operating Characteristic (ROC) curve and the area under the ROC (AUC) are widely used measure of accuracy for diagnostic test to distinguish between two populations
The objective of this paper is to investigate the parametric and beta ROC regression models when compared with the AUC regression model presented by (Zhang et al, 2011) using data from a clinical trial concerning the efficacy of an active drug to treat stress urinary incontinence in North American women
Since we do not have access to the same sample used by (Zhang et al, 2011), we present the results for the data set that we have access (n = 2200) and for four (4) random subsets of size 420 with a 1:1 split for the treatment and control, in hopes of understanding data variability and dependency on the performance of the ROC regression methods
Summary
The Receiver Operating Characteristic (ROC) curve and the area under the ROC (AUC) are widely used measure of accuracy for diagnostic test to distinguish between two populations. An important application of ROC curve is to determine how a test’s performance is affected by covariates. One approach is to model the AUC of the ROC curve by modifying the MannWhitney statistics (MW) as a GLM (Pepe, 2003). Another approach is to model the ROC directly. Dodd and Pepe (2003) proposed a Generalized Linear Model (GLM) framework to directly model the ROC with covariates as follows:. For t∈ (0,1) where g-1 is a monotone link function, X is a vector of covariates, h0(⋅) is an unknown monotonic increasing function and b is a vector of the model parameters. The two models differ, they are both based upon the conditional expectation of Mann-Whitney U-statistic
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
