Specificity
Updates to Article Attributes
Specificity is one of the 4 basic diagnostic test metrics in addition to sensitivity, positive predictive value and negative predictive value. Specificity is a measure of how good a diagnostic test/investigation is at identifying people who are healthy and is calculated by dividing the abilitynumber of true negatives (TN) by the number of people without disease, i.e. true negatives and false positives (FP):
TN/(TN + FP)
The formula shows that a high specificity is achieved by maximising true negatives and minimising false positives. Because highly specific tests have few false positives, a specific test when positive may be used to “rule in” disease since a positive result is likely to be correctlya true positive.
Specificity is also called the true negative (truerate and can be expressed as a conditional probability:
P(Test negative|Disease negative)
In an ROC curve, 1-specificity is plotted along the disease
where, total cases withoutx-axis and can be renamed the disease =false positive rate, the opposite of the true negative + false positive negative rate.
-<p><strong>Specificity </strong>of a test/investigation is the ability of a test to be correctly negative (true negative) in persons without the disease in question.</p><h4>Calculation</h4><p>Specificity = true negatives detected by test / total cases without the disease</p><p>where, total cases without the disease = true negative + false positive</p>- +<p><strong>Specificity </strong>is one of the 4 basic diagnostic test metrics in addition to <a href="/articles/sensitivity" title="Sensitivity">sensitivity</a>, <a href="/articles/positive-predictive-value" title="Positive predictive value">positive predictive value</a> and <a href="/articles/negative-predictive-value" title="Negative predictive value">negative predictive value</a>. Specificity is a measure of how good a diagnostic test is at identifying people who are healthy and is calculated by dividing the number of true negatives (TN) by the number of people without disease, i.e. true negatives and false positives (FP):</p><ul><li><p>TN/(TN + FP)</p></li></ul><p>The formula shows that a high specificity is achieved by maximising true negatives and minimising false positives. Because highly specific tests have few false positives, a specific test when positive may be used to “rule in” disease since a positive result is likely to be a true positive.</p><p>Specificity is also called the true negative rate and can be expressed as a <a href="/articles/conditional-probability" title="Conditional probability">conditional probability</a>:</p><ul><li><p>P(Test negative|Disease negative)</p></li></ul><p>In an <a href="/articles/receiver-operating-characteristic-curve" title="ROC curve">ROC curve</a>, 1-specificity is plotted along the x-axis and can be renamed the false positive rate, the opposite of the true negative rate.</p>
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