Sensitivity and specificity are often used to determine the effectiveness of a medical test.
Sensitivity and specificity are used to determine the effectiveness of a test, especially medical tests in the diagnosis of a disease. Sensitivity shows the test's ability to diagnose correctly the patients with the disease. Specificity determines the test's ability to determine the patients who do not have the disease, or are disease-free. Other statistical measures commonly used in medicine with sensitivity and specificity are positive and negative predictive values. A positive predictive value is the chance that a positive test is accurate, while a negative predictive value is the chance that a negative result is correct and the patient does not have the disease.
Instructions
Sensitivity Calculation
1. Determine the true positives; that is, the patient has the disease and the test is positive. Determine the false negatives; the patient has the disease, but the test is negative.
2. Add the true positives and false negatives together. Divide the true positives by this result.
3. Describe the value as a percentage. A result of 75 percent predicts that 75 percent with a positive test have the disease, while 25 percent with a negative test result will also have the disease, and the test has misdiagnosed them.
Specificity Calculation
4. Determine the true negatives: The patient does not have the disease and the test result was correctly negative. Determine the false positives: The patient has a positive test but does not have the disease.
5. Add the true negatives and false positives together. Divide the true negatives with this result to give the specificity of the test.
6. Describe the answer as a percentage; a value of 75 percent correctly determines that 75 percent of patients with a negative result do not have the disease, while 25 percent of patients with a negative result actually have the disease.
Tags: have disease, disease test, does have, does have disease, negative result, patients with, positive test
