Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests

Abstract

* Diagnostic accuracy studies address how well a test identifies the target condition of interest. * Sensitivity, specificity, predictive values and likelihood ratios (LRs) are all different ways of expressing test performance. * Receiver operating characteristic (ROC) curves compare sensitivity versus specificity across a range of values for the ability to predict a dichotomous outcome. Area under the ROC curve is another measure of test performance. * All of these parameters are not intrinsic to the test and are determined by the clinical context in which the test is employed. * High sensitivity corresponds to high negative predictive value and is the ideal property of a "rule-out" test. * High specificity corresponds to high positive predictive value and is the ideal property of a "rule-in" test. * LRs leverage pre-test into post-test probabilities of a condition of interest and there is some evidence that they are more intelligible to users.

Figures

Figure 1

ROC curve for various cut-off levels of BNP in differentiating between dyspnoea due to congestive heart failure and dyspnoea due to other causes. Copyright © 2002 Massachusetts Medical Society. All rights reserved.

Figure 2

An example of Fagan’s nomogram. Prior probability is indicated on the vertical axis on the left of the nomogram and a line can be drawn through the BNP value in the middle (note the logarithmic scale) and extrapolated to the point where it intercepts the vertical axis on the right of the nomogram which corresponds to post-test probability. Source: BMJ, 2004, 329, 168-9. Reproduced with permission from the BMJ Publishing Group.