Which statement describes PPV and NPV definitions?

• A. A positive predictive value is the probability that a person with a positive test truly has the disease; a negative predictive value is the probability that a person with a negative test truly does not have the disease.
• B. A positive predictive value is the probability that a person with a negative test truly does not have the disease; a negative predictive value is the probability that a person with a positive test truly has the disease.
• C. PPV and NPV are independent of disease prevalence.
• D. PPV and NPV are both determined solely by test sensitivity and specificity.

Answer: (A)

Positive predictive value is the probability that a person with a positive test actually has the disease, and negative predictive value is the probability that a person with a negative test truly does not have the disease. These definitions are the reason that this option is correct. PPV and NPV reflect not only the test's ability to identify disease (sensitivity) and to rule it out (specificity) but also how common the disease is in the population being tested. When disease prevalence is low, PPV tends to be lower because many positives may be false positives, while NPV tends to be higher because most people are truly disease-free. In contrast, sensitivity and specificity are properties of the test itself and do not depend on how common the disease is. That’s why the other statements are not correct: they either mix up the meanings of PPV/NPV, claim independence from prevalence, or say these values are determined only by sensitivity and specificity.