Which statement correctly defines Positive Predictive Value?

Which statement correctly defines Positive Predictive Value?

• A. It is the probability that subjects with a positive test actually have the disease
• B. It is the probability that subjects with a negative test do not have the disease
• C. It is the proportion of true negatives among all negative results
• D. It is the proportion of false positives among all positives

Answer: (A)

Positive Predictive Value is the likelihood that someone truly has the disease after testing positive. It is the proportion of true positives among all positive results (TP divided by TP plus FP). This means a positive result is more convincing when PPV is high, which happens when the disease is common or when the test has strong specificity. So even a good test can give a lower PPV in a population where the disease is rare, due to more false positives. The other descriptions refer to different ideas: one describes the negative predictive value (the chance someone with a negative test truly does not have the disease), and the other describes the rate of false positives among positives, which is not what PPV measures.

Positive Predictive Value is the likelihood that someone truly has the disease after testing positive. It is the proportion of true positives among all positive results (TP divided by TP plus FP). This means a positive result is more convincing when PPV is high, which happens when the disease is common or when the test has strong specificity. So even a good test can give a lower PPV in a population where the disease is rare, due to more false positives. The other descriptions refer to different ideas: one describes the negative predictive value (the chance someone with a negative test truly does not have the disease), and the other describes the rate of false positives among positives, which is not what PPV measures.