Which statement correctly defines Negative Predictive Value?

Which statement correctly defines Negative Predictive Value?

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

Answer: (A)

Negative Predictive Value is the probability that a person who tests negative truly does not have the disease. It is the fraction of true negatives among all negative test results (TN ÷ [TN + FN]). In other words, after a negative result, NPV tells you how confident you can be that you’re disease-free. This value rises when the disease is rare in the population and when the test performs well (high sensitivity to reduce false negatives and high specificity to reduce false positives, with the overall effect modulated by prevalence). Other statements describe different concepts, such as what a positive test implies or the proportion of true positives among positives; those are not what NPV measures.

Negative Predictive Value is the probability that a person who tests negative truly does not have the disease. It is the fraction of true negatives among all negative test results (TN ÷ [TN + FN]). In other words, after a negative result, NPV tells you how confident you can be that you’re disease-free. This value rises when the disease is rare in the population and when the test performs well (high sensitivity to reduce false negatives and high specificity to reduce false positives, with the overall effect modulated by prevalence). Other statements describe different concepts, such as what a positive test implies or the proportion of true positives among positives; those are not what NPV measures.