Title

Using the ROC Curve to Measure Association and Evaluate Prediction Accuracy for a Binary Outcome

Document Type

Article

Publication Date

3-15-2017

Publication Title

Biometrics and Biostatistics International Journal

DOI

10.15406/bbij.2017.05.00134

ISSN

2378-315X

Abstract

This review article addresses the ROC curve and its advantage over the odds ratio to measure the association between a continuous variable and a binary outcome. A simple parametric model under the normality assumption and the method of Box-Cox transformation for non-normal data are discussed. Applications of the binormal model and the Box-Cox transformation under both univariate and multivariate inference are illustrated by a comprehensive data analysis tutorial. Finally, a summary and recommendations are given as to the usage of the binormal ROC curve.

Comments

This is an open access article, first published in Biometrics and Biostatistics International Journal.

MedCrave is Open Access which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from us or the author.

Share

COinS