Using the ROC Curve to Measure Association and Evaluate Prediction Accuracy for a Binary Outcome
Biometrics and Biostatistics International Journal
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.
Yin, Jingjing, Robert L. Vogel.
"Using the ROC Curve to Measure Association and Evaluate Prediction Accuracy for a Binary Outcome."
Biometrics and Biostatistics International Journal, 5 (3): 1-10: MedCrave Group.