Explain positive predictive value (PPV) and negative predictive value (NPV) and how disease prevalence affects them.

Explain positive predictive value (PPV) and negative predictive value (NPV) and how disease prevalence affects them.

• A. PPV = probability a positive test is a true case; NPV = probability a negative test is a true non-case; Both are independent of prevalence.
• B. PPV = probability a positive test is a true case; NPV = probability a negative test is a true non-case; Both depend on disease prevalence.
• C. PPV = probability of disease given a positive test; NPV = probability of non-disease given a negative test; Both depend on test specificity.
• D. PPV = probability of false positive given a positive test; NPV = probability of false negative given a negative test; Both depend on prevalence.

Answer: (B)

PPV and NPV are about what a test result means for the individual. Positive predictive value is the probability that a person truly has the disease given that their test result is positive. Negative predictive value is the probability that a person truly does not have the disease given that their test result is negative. These are conditional probabilities that hinge on how common the disease is in the population (prevalence) as well as on the test’s performance (sensitivity and specificity). The prevalence of disease shifts these values because it changes the balance between true and false results. In a setting where the disease is rare, even a good test will yield relatively more false positives compared with true positives, so PPV tends to be low, while NPV tends to be very high. In a setting with higher disease prevalence, PPV rises because positive results are more likely to be true positives, while NPV falls because a negative result is more likely to miss a true case. For example, with common test performance, a very low-prevalence population might have a PPV around a modest fraction, while a high-prevalence population pushes PPV toward higher values; NPV would be high when prevalence is low and decrease as prevalence increases. This illustrates why the correct description states that PPV is the probability a positive test is a true case and NPV is the probability a negative test is a true non-case, and both depend on disease prevalence (along with the test’s sensitivity and specificity).

PPV and NPV are about what a test result means for the individual. Positive predictive value is the probability that a person truly has the disease given that their test result is positive. Negative predictive value is the probability that a person truly does not have the disease given that their test result is negative. These are conditional probabilities that hinge on how common the disease is in the population (prevalence) as well as on the test’s performance (sensitivity and specificity).

The prevalence of disease shifts these values because it changes the balance between true and false results. In a setting where the disease is rare, even a good test will yield relatively more false positives compared with true positives, so PPV tends to be low, while NPV tends to be very high. In a setting with higher disease prevalence, PPV rises because positive results are more likely to be true positives, while NPV falls because a negative result is more likely to miss a true case. For example, with common test performance, a very low-prevalence population might have a PPV around a modest fraction, while a high-prevalence population pushes PPV toward higher values; NPV would be high when prevalence is low and decrease as prevalence increases. This illustrates why the correct description states that PPV is the probability a positive test is a true case and NPV is the probability a negative test is a true non-case, and both depend on disease prevalence (along with the test’s sensitivity and specificity).