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Sensitivity and specificity
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Let’s say a new screening test is developed to figure out if people have diabetes before they start showing symptoms. Before using the test, we have to make sure that the test works - in other words, can the test correctly identify if a person has diabetes or not? This is the test’s validity, and it has two components - sensitivity and specificity.
A test with high sensitivity will correctly identify most people who have the condition, and a test with high specificity will correctly identify most people who don’t have the disease.
We can organize the results using a 2 by 2 table, where the true disease status, positive or negative, of the individual is on the top of the box and the results of the screening test, positive or negative, are on the side, and each of the cells is labeled a, b, c, or d. In this situation, a positive test indicates that a person has diabetes.
And lastly a person who gets a negative test result even though they have diabetes, would be a false negative.
Sensitivity and specificity are two important statistical measures used to evaluate the performance of medical tests, such as diagnostic tests for diseases.
Sensitivity measures the ability of a test to correctly identify those who have the disease. It is the proportion of people with a disease who test positive. This means that a test with high sensitivity can correctly identify most individuals who have the disease, while a test with low sensitivity will miss many cases of the disease. Sensitivity is calculated by dividing the number of true positives, by the total number of all people who have the condition - the true positives and false negatives.
Specificity, on the other hand, measures the ability of a test to correctly identify those who do not have the disease. It is the proportion of people without a disease who correctly test negative. This means that a test with high specificity can correctly identify most individuals who do not have the disease, while a test with low specificity will result in many false positive results. Specificity is calculated by dividing the number of true negatives by the total number of all people who don't have the condition - the true negatives and false positives.
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