Quantifying how tests reduce diagnostic uncertainty

Malcolm G Coulthard ^1*

¹ Royal Victoria Infirmary, United Kingdom

^* To whom correspondence should be addressed. E-mail: malcolm.coulthard{at}nuth.nhs.uk.

Accepted 30 November 2006

Abstract

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. We present graphs that demonstrate this, and that allow easy reference to appropriate values.

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 their likely clinical prevalence. PRU plots demonstrate the discriminatory value of tests more clearly than sensitivity and specificity which they are derived from, and should be published alongside them.

Keywords: predictive value, probability, sensitivity, specificity

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