Definition: If you test positive for a disease, the negative predictive value tells you how likely it is that you actually don't have the disease. It is defined as the number of true negatives (people who test negative who are not infected) divided by the total number of people who test negative, and it varies with test sensitivity, test specificity, and disease prevalence as you can see in the example below. Because of the dependence on disease prevalence in the community where they work, most doctors cannot simply give you a number for the negative predictive value when you go in for any given test -- even if they know the sensitivity and specificity.
Alternate Spellings: NPV
Examples:
If a chlamydia test has 80% sensitivity & 80% specificity in a population of 100 with a chlamydia prevalence of 10%:
8 out of 10 true positives test positive
72 out of 90 true negatives test negative
Out of 74 negative tests, 82 are true negatives and 2 are false negatives. Therefore, the negative predictive value (NPV) would be 97%(72/74). 97% of people who test negative would actually be negative for chlamydia.
