The clinical value of a test is related to its sensitivity, its specificity, and the incidence of the disease in the population tested.
Specificity refers to the ability of a test to correctly identify individuals who do not have the disease. The formula for specificity is as follows:
One hundred percent specificity would indicate there are no false positives—that is, the test identifies all individuals who do not have the disease.
Sensitivity refers to the ability of a test to correctly identify individuals who have the disease. The formula for sensitivity is as follows:
One hundred percent sensitivity would indicate there are no false negatives—that is, the test identifies all individuals with the disease as having the disease.
True positive: positive test result in a person with the disease
True negative: negative test result in a person without the disease
False positive: positive test result in a person without the disease
False negative: negative test result in a person with the disease
Sensitivity and specificity do not change with different populations of ill and healthy patients.
Incidence refers to the number of new cases of a disease, during a specified period of time, in a specified population or community.
Prevalence refers to the number of existing cases of a disease, at a specific period of time, in a given population.
Predictive values refer to the ability of a screening test result to correctly identify the disease state. The predictive value of a test can be very different when applied to people of differing ages, gender, geographic locations, and cultures. True-positive results correctly identify individuals who actually have the disease, and true-negative results correctly identify individuals who do not actually have the disease. Positive predictive value equals the percentage of positive tests with true-positive results (i.e., the individual does have the disease). Negative predictive value refers to the percentage of negative tests with true-negative results (i.e., the individual does not have the disease).
See Table 1.3 for an example that demonstrates the specificity, sensitivity, and predictive values for a screening test to identify the cystic fibrosis gene.
Thus, this screening test will give a false-negative result about 20% of the time (e.g., the person does have the cystic fibrosis gene but the test results are negative).
Thus, there is an 8% chance that the person will test positive for the cystic fibrosis gene but does not have it.
Thus, there is a 5% chance that the person will test negative for the cystic fibrosis gene but actually does have it.