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Overview Overview
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Calculating sensitivity and specificity Calculating sensitivity and specificity
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Cite
Extract
Overview
These two measures are closely related to the concepts of type I and type II errors.
Imagine a study evaluating a new screening test for ovarian cancer. Each person taking the test either has or does not have ovarian cancer. The test results for each subject may or may not match the subject's actual status. In that setting:
...
Calculating sensitivity and specificity
Sensitivity measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of people with ovarian cancer who are correctly identified as having the condition).
This can be calculated using this equation:
Sensitivity = No. true +ves/(No. true +ves + No. false –ves)
Therefore, if a test has a high sensitivity, a negative result would provide a level of confidence of the absence of disease. If any given test has 100% sensitivity, then all people with the disease will be identified, but some may be identified in error. In order to make a decision on the clinical utility of this test, one must be able to quantify the number of patients who are incorrectly identified. Therefore, the sensitivity alone cannot be used to determine whether a test is useful in practice.
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