The negative predictive value is defined as the number of true negatives (people who test negative who don’t have a condition) divided by the total number of people who test negative. It varies with test sensitivity, test specificity, and disease prevalence.
What affects the predictive value of a test?
Predictive Value of a Test Result
Where Probability of disease represents the proportion of the pre-test population harboring the disease. From this formula, one sees that the predictive value is affected by both the sensitivity/specificity of the test itself and the pre-test probability.
How does prevalence affect negative predictive value?
Prevalence thus impacts the positive predictive value (PPV) and negative predictive value (NPV) of tests. As the prevalence increases, the PPV also increases but the NPV decreases. Similarly, as the prevalence decreases the PPV decreases while the NPV increases.
Why is predictive value negative?
Positive predictive value is the probability that subjects with a positive screening test truly have the disease. … Negative predictive value is the probability that subjects with a negative screening test truly don’t have the disease.
What does it mean when the negative predictive value is high?
The more sensitive a test, the less likely an individual with a negative test will have the disease and thus the greater the negative predictive value. The more specific the test, the less likely an individual with a positive test will be free from disease and the greater the positive predictive value.
How can you improve positive predictive value?
You can improve predictive value by first narrowing down the population to be tested with standard history and physical exam (e.g., don’t order superfluous lab tests).
What is the false negative rate?
The reported rate of false negatives is 20%. However, the range of false negatives is from 0% to 30%, depending on the study and when in the course of infection the test is performed. Research suggests antibody levels may wane over just a few months.