The meaning and use of the area under a receiver operating characteristic (ROC) curve.

  title={The meaning and use of the area under a receiver operating characteristic (ROC) curve.},
  author={James A. Hanley and Barbara J. McNeil},
  volume={143 1},
Key ResultA representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject.Expand
Direct estimation of the area under the receiver operating characteristic curve in the presence of verification bias.
A new method for directly estimating the AUC in the setting of verification bias based on U-statistics and inverse probability weighting (IPW) is developed and it is shown that the new estimator is equivalent to the empirical AUC derived from the bias-corrected ROC curve arising from the IPW approach. Expand
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data. Expand
An Evaluation of Methods for Estimating the Area Under the Receiver Operating Characteristic (ROC) Curve
  • R. Centor, J. Schwartz
  • Medicine
  • Medical decision making : an international journal of the Society for Medical Decision Making
  • 1985
Generally, the nonparametric method yields lower area estimates than the maximum-likelihood-estimation technique, however, these differences generally were small, particularly with ROC curves derived from five or more cutoff points. Expand
The ROC Curve and the Area under It as Performance Measures
Abstract The receiver operating characteristic (ROC) curve is a two-dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet ofExpand
Discrimination Index, the Area Under the ROC Curve
The accuracy of fit of a mathematical predictive model is the degree to which the predicted values coincide with the observed outcome. When the outcome variable is dichotomous and predictions areExpand
Exact Bootstrap Variances of the Area Under ROC Curve
The area under the Receiver Operating Characteristic (ROC) curve (AUC) and related summary indices are widely used for assessment of accuracy of an individual and comparison of performances ofExpand
The Stata Journal
The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictiveExpand
Estimating the Area under a Receiver Operating Characteristic Curve For Repeated Measures Design
The receiver operating characteristic (ROC) curve is widely used for diagnosing as well as for judging the discrimination ability of different statistical models. Although theories about ROC curvesExpand
Assessing classifiers in terms of the partial area under the ROC curve
The true performance (not its estimate) of classifiers measured in ''separability'' may vary significantly with varying the training set, while its estimate yet has a small estimated variance, which accounts for having ''good'' estimate for ''bad'' performance. Expand
ROC Curves and the Areas under Them for Dichotomized Tests
The authors present equations for estimating the area under the ROC curve and the AUC when only one pair of a true- and a false-positive rate is given, for inherently logistically and normally distributed data. Expand


Basic principles of ROC analysis.
  • C. Metz
  • Medicine
  • Seminars in nuclear medicine
  • 1978
ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making and the concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error. Expand
The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
Abstract Receiver operating characteristic graphs are shown to be a variant form of ordinal dominance graphs. The area above the latter graph and the area below the former graph are useful measuresExpand
Statistical significance tests for binormal ROC curves
Abstract Statistical significance tests are derived and evaluated for measuring apparent differences between an obtained and an expected binormal ROC curve, between two independent binormal ROCExpand
ROC analysis applied to the evaluation of medical imaging techniques.
  • J. Swets
  • Medicine
  • Investigative radiology
  • 1979
This paper presents a brief description of the ROC, and shows how it provides a measure of diagnostic accuracy that is free of judgmental bias. Expand
Radiographic applications of receiver operating characteristic (ROC) curves.
The basic concepts underlying the theory and experimental determination of receiver operating characteristic (ROC) curves are discussed and such curves were used to describe the detectability of the image of 2 min Lucite beads in a noisy background of radiographic mottle. Expand
Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data
Abstract Procedures have been developed for obtaining maximum-likelihood estimates of the parameters of the Thurstonian model for the method of successive intervals. The signal-detection model forExpand
Assessment of diagnostic technologies.
A general protocol for rigorous evaluation of diagnostic systems in medicine was applied successfully in a comparative study of two radiologic techniques, and computed tomography was found to be substantially more accurate than radionuclide scanning. Expand
Signal detection theory and psychophysics
This book discusses statistical decision theory and sensory processes in signal detection theory and psychophysics and describes how these processes affect decision-making. Expand
Numbcr I Hanler
  • Numbcr I Hanler
  • 1982