title: Comparing the areas under two correlated ROC curves: parametric and non-parametric approaches.
abstract: In order to compare the discriminatory effectiveness of two diagnostic markers the equality of the areas under the respective Receiver Operating Characteristic Curves is commonly tested. A non-parametric test based on the Mann-Whitney statistic is generally used. Weiand et al. (1989) present a parametric test based on normal distributional assumptions. We extend this test using the Box-Cox power family of transformations to non-normal situations. These three test procedures are compared in terms of significance level and power by means of a large simulation study. Overall we find that transforming to normality is to be preferred. An example of two pancreatic cancer serum biomarkers is used to illustrate the methodology.
authors.affiliation: Department of Statistics, University of Haifa 31905, Israel.
authors.forename: Katy
authors.initials: K
authors.lastname: Molodianovitch
authors.forename: David
authors.initials: D
authors.lastname: Faraggi
authors.forename: Benjamin
authors.initials: B
authors.lastname: Reiser
mesh_terms: D019540:Area Under Curve; D015415:Biomarkers; D018394:CA-125 Antigen / Q000097:blood; D018395:CA-19-9 Antigen / Q000097:blood; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D010190:Pancreatic Neoplasms / Q000175:diagnosis; D012372:ROC Curve; D018709:Statistics, Nonparametric
chemical_list: D015415:Biomarkers; D018394:CA-125 Antigen; D018395:CA-19-9 Antigen