Background: Diagnostic tests are commonly evaluated from sensitivity and specificity, which are robust and independent of prevalence, but clinically not intuitive. Many clinicians prefer to use positive and negative predictive values (PPV and NPV), but they are frequently applied as if they are independent of prevalence, whereas this may make an important difference.
Methods and results: We present graphs that allow easy reference to appropriate values and demonstrate that PPV and NPV are not independent of prevalence. PPV and NPV figures reflect the prior probability of the case having a positive diagnosis, estimated clinically from the history, examination and other results, as well as the impact of the test result. To avoid the common error of allowing for prior probability twice, and to interpret the impact of the test result alone, we present graphs of the proportionate reduction in uncertainty score (PRU), calculated from sensitivity, specificity and prevalence. These plots show the extent to which either a positive or negative test result affects the remaining degree of uncertainty about a diagnosis in either direction, according to likely clinical prevalence.
Conclusions: PRU plots demonstrate the discriminatory value of tests more clearly than sensitivity and specificity from which they are derived, and should be published alongside them.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.