Statistics Notes: Diagnostic tests 2: predictive values

TLDR

Diagnostic tests aim to determine correct diagnoses, but sensitivity and specificity alone are insufficient; predictive values, such as positive predictive value (proportion of true positives among all positives) and negative predictive value (proportion of true negatives among all negatives), provide the needed probabilities. Using data from 263 patients with abnormal liver scans, 231 had abnormal pathology, yielding a positive predictive value of 0.88. Other details are not provided.

Abstract

The whole point of a diagnostic test is to use it to make a diagnosis, so we need to know the probability that the test will give the correct diagnosis. The sensitivity and specificity1 do not give us this information. Instead we must approach the data from the direction of the test results, using predictive values. Positive predictive value is the proportion of patients with positive test results who are correctly diagnosed. Negative predictive value is the proportion of patients with negative test results who are correctly diagnosed. Using the same data as in the previous note,1 we know that 231 of 263 patients with abnormal liver scans had abnormal pathology, giving the proportion of correct diagnoses as 231/263 = 0.88. …