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.
Recommended Citation
Yin, Jingjing, Robert L. Vogel.
2017.
"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.
doi: 10.15406/bbij.2017.05.00134
https://digitalcommons.georgiasouthern.edu/biostat-facpubs/191
Comments
This is an open access article, published in Biometrics and Biostatistics International Journal.
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