Confidence bounds when the estimated ROC area is 1.01.
OBJECTIVE
In studies with small samples, the authors often encounter data sets in which the estimated area under the receiver operating characteristic (ROC) curve is 1.0. In such cases, neither asymptotic nor resampling methods provide a means of estimating the standard error or constructing a lower confidence bound. The purpose of this study was to develop tables for determining the approximate 95% lower confidence bound when the estimated ROC area is 1.0.


METHODS
Using Monte Carlo simulation, the authors generated 10,000 data sets for each specification of sample sizes, ROC curve shape, and data format (continuous and ordinal scale). For each of these combinations the authors determined the 95% lower confidence bound.


RESULTS
When the estimated ROC area is 1.0, the 95% lower confidence bounds differ dramatically depending on the shape of the ROC curve and on whether the test results are ordinal or continuous. Four tables of 95% lower confidence bounds are provided, along with guidelines for their use.


CONCLUSIONS
Given the different shapes of ROC curves and the different formats in which ROC data are collected, it is not feasible to offer one simple method of constructing confidence bounds that works for all ROC curves. The tables provided in this article are useful for interpreting studies with estimated ROC areas of 1.0.
Obuchowski
Nancy A
NA
Department of Biostatistics and Epidemiology The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland OH, 44195, USA.
Lieber
Michael L
ML
D019540:Area Under Curve; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012372:ROC Curve; D018401:Sample Size; D015983:Selection Bias