The following terms are fundamental to understanding the utility of clinical tests: When evaluating a clinical test, the terms sensitivity and specificity are used. They are independent of the population of interest subjected to the test. The terms positive predictive value (PPV) and negative predictive value (NPV) are used when considering the value of a test to a clinician and are dependent on the prevalence of the disease in the population of interest.
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True positive: the patient has the disease and the test is positive.
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False positive: the patient does not have the disease but the test is positive.
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True negative: the patient does not have the disease and the test is negative
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False negative: the patient has the disease but the test is negative.
The sensitivity of a clinical test refers to the ability of the test to correctly identify those patients with the disease. A test with 100% sensitivity correctly identifies all patients with the disease. A test with 80% sensitivity detects 80% of patients with the disease (true positives) but 20% with the disease go undetected (false negatives). A high sensitivity is clearly important where the test is used to identify a serious but treatable disease (e.g. cervical cancer). Screening the female population by cervical smear testing is a sensitive test. However, it is not very specific and a high proportion of women with a positive cervical smear who go on to have a colposcopy are ultimately found to have no underlying pathology.
Specificity
The specificity of a clinical test refers to the ability of the test to correctly identify those patients without the disease.
Therefore, a test with 100% specificity correctly identifies all patients without the disease. A test with 80% specificity correctly reports 80% of patients without the disease as test negative (true negatives) but 20% patients without the disease are incorrectly identified as test positive (false positives).As discussed above, a test with a high sensitivity but low specificity results in many patients who are disease free being told of the possibility that they have the disease and are then subject to further investigation. Although the ideal (but unrealistic) situation is for a 100% accurate test, a good alternative is to subject patients who are initially positive to a test with high sensitivity/low specificity, to a second test with low sensitivity/high specificity. In this way, nearly all of the false positives may be correctly identified as disease negative.
Positive predictive value
The PPV of a test is a proportion that is useful to clinicians since it answers the question: ‘How likely is it that this patient has the disease given that the test result is positive?’
Negative predictive value
The NPV of a test answers the question: ‘How likely is it that this patient does not have the disease given that the test result is negative?’