### Abstract

- Top of page
- Abstract
- INTRODUCTION
- SENSITIVITY, SPECIFICITY AND THE CUT-OFF POINT
- THE ROC CURVE
- WHAT CUT-OFF POINT SHOULD BE USED IN PRACTICE?
- CONFIDENCE INTERVALS
- CONCLUSION
- References

The results of many clinical tests are quantitative and are provided on a continuous scale. To help decide the presence or absence of disease, a cut-off point for ‘normal’ or ‘abnormal’ is chosen. The sensitivity and specificity of a test vary according to the level that is chosen as the cut-off point. The receiver operating characteristic (ROC) curve, a graphical technique for describing and comparing the accuracy of diagnostic tests, is obtained by plotting the sensitivity of a test on the *y* axis against 1-specificity on the *x* axis. Two methods commonly used to establish the optimal cut-off point include the point on the ROC curve closest to (0, 1) and the Youden index. The area under the ROC curve provides a measure of the overall performance of a diagnostic test. In this paper, the author explains how the ROC curve can be used to select optimal cut-off points for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of tests.

Conclusion: The ROC curve is obtained by calculating the sensitivity and specificity of a test at every possible cut-off point, and plotting sensitivity against 1-specificity. The curve may be used to select optimal cut-off values for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of different tests.