The proportion of individuals with a negative test primarily affects which predictive value?

• A. PPV
• B. Specificity
• C. Sensitivity
• D. NPV

Answer: (D)

Focusing on predictive values, the main idea is that a negative predictive value answers “Among those who test negative, how many truly do not have the disease?” This makes it directly tied to how many people end up with a negative result. NPV is calculated as true negatives divided by all negatives (true negatives plus false negatives). Therefore, when a larger proportion of individuals receive a negative test, there are more cases in the denominator, and the NPV reflects that negative-result group. In practice, NPV tends to be higher when disease prevalence is low, because a larger share of those who test negative are truly disease-free. The other measures are more about the test’s inherent ability to identify disease among those tested (sensitivity) or among those without disease (specificity), and they don’t hinge on how many people end up testing negative in the tested population. PPV, for instance, concerns positives and the probability that a positive result is truly diseased.