To calculate the positive predictive value or negative predictive value of a diagnostic test, which parameter must be known?

• A. Test sensitivity
• B. Specificity
• C. Prevalence of disease
• D. Likelihood ratio

Answer: (C)

Predictive values reflect how a test performs in a real patient, and they depend on how common the disease is in the group being tested. Sensitivity and specificity describe the test’s inherent performance, but PPV and NPV require knowing the pretest probability of disease—i.e., the prevalence in the tested population.

Using Bayes’ framework, PPV = (sensitivity × prevalence) / [sensitivity × prevalence + (1 − specificity) × (1 − prevalence)], and NPV = (specificity × (1 − prevalence)) / [(1 − sensitivity) × prevalence + specificity × (1 − prevalence)]. When disease prevalence is low, a positive result is more likely a false positive, lowering PPV; when prevalence is high, PPV rises and NPV falls.

Thus, to calculate PPV or NPV you must know the prevalence of disease in the population being tested.