This is a measure of accuracy for screening tests that refers to true negatives — the probability that subjects with a negative result test really do not have the disease or condition. So if a person is tested and receives a negative result, how likely is it that the result is correct? How relieved can they be? Knowing the negative predictive value (NPV) can determine how many false negatives are occurring with a test.
Deeper dive
This is a measure of accuracy for screening tests that refers to true negatives — the probability that subjects with a negative result test really do not have the disease or condition. So if a person is tested and receives a negative result, how likely is it that the result is correct? How relieved can they be? Knowing the negative predictive value (NPV) can determine how many false negatives are occurring with a test.
Negative predictive value is derived from the same numbers needed to quantify the sensitivity and specificity of a test, but NPV also depends on the population being tested and the prevalence of the disease in that population. For example, if you are testing for hepatitis C in a population of men who have HIV and have a history of injectable illicit drug use, the negative predictive value, the NPV may not be very high because there will likely be a lot of false negatives in a group that is very high risk for the condition being screened. On the other hand, if you’re screening for hepatitis C in newborns, the NPV is likely to be extremely high—it’s very unlikely there will be many false negatives in that group.