Abstract
The area under the ROC curve is a common index summarizing the information contained in the curve. When comparing two ROC curves, though, problems arise when interest does not lie in the entire range of false-positive rates (and hence the entire area). Numerical integration is suggested for evaluating the area under a portion of the ROC curve. Variance estimates are derived. The method is applicable for either continuous or rating scale binormal data, from independent or dependent samples. An example is presented which looks at rating scale data of computed tomographic scans of the head with and without concomitant use of clinical history. The areas under the two ROC curves over an a priori range of false- positive rates are examined, as well as the areas under the two curves at a specific point.
Keywords
Receiver operating characteristicRange (aeronautics)StatisticsVariance (accounting)Scale (ratio)MathematicsA priori and a posterioriSensitivity (control systems)Area under the curveArtificial intelligencePattern recognition (psychology)Computer scienceMedicineCartographyGeography
Affiliated Institutions
Related Publications
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
Receiver operating characteristic (ROC) curves are used to describe and compare the performance of diagnostic technology and diagnostic algorithms. This paper refines the statis...
Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed...
An Evaluation of Methods for Estimating the Area Under the Receiver Operating Characteristic (ROC) Curve
The area under the receiver operating characteristic (ROC) curve serves as one means for evaluating the performance of diagnostic and predictive test systems. The most commonly ...
Receiver operating characteristic curve: overview and practical use for clinicians
Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and r...
Estimation and detection of event-related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach
Recent developments in analysis methods for event-related functional magnetic resonance imaging (fMRI) has enabled a wide range of novel experimental designs. As with selective ...
Publication Info
- Year
- 1989
- Type
- article
- Volume
- 9
- Issue
- 3
- Pages
- 190-195
- Citations
- 699
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
699
OpenAlex
Cite This
Donna K. McClish
(1989).
Analyzing a Portion of the ROC Curve.
Medical Decision Making
, 9
(3)
, 190-195.
https://doi.org/10.1177/0272989x8900900307
Identifiers
- DOI
- 10.1177/0272989x8900900307