A conditional nonparametric test for comparing two areas under the ROC curves from a paired design.
OBJECTIVE
To develop a conditional nonparametric procedure for comparing two correlated areas under receiver operating characteristic (ROC) curves (AUC).


METHODS
A nonparametric conditional test to compare areas under two ROC curves was developed using the distribution of the elements of the nonparametric AUC estimators in a permutation space. The conditioning is made on the observed discordances between the relative orderings of ratings of the normal and abnormal cases for the two modalities taken over all possible pairs. The type I error of the procedure was verified using computer simulations. The power of the test was compared with an existing unconditional procedure on simulated datasets from binormal distributions as well as from a mixture of binormal distributions of ratings.


RESULTS
The proposed test is conservative for low sample sizes, large AUC, and high correlation between modalities. It possesses a reasonable type I error for sample sizes as low as 20 actually positive and 20 actually negative cases. In plausible situations in which the sample in observer performance studies can not be monotonically transformed into a binormal distribution, this approach may have modest power advantages over the conventional nonparametric test.


CONCLUSIONS
The conditional nonparametric test presented here is an alternative approach to existing unconditional procedures and may offer advantages in certain types of observer performance studies.
Bandos
Andriy I
AI
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA. anb61+pitt.edu
Rockette
Howard E
HE
Gur
David
D
D019540:Area Under Curve; D003198:Computer Simulation; D006801:Humans; D016555:Matched-Pair Analysis; D015233:Models, Statistical; D016011:Normal Distribution; D012372:ROC Curve; D018401:Sample Size; D018709:Statistics, Nonparametric; D013679:Technology, Radiologic / Q000706:statistics & numerical data*