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Sensitivity and specificity

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This is a draft article, under development. These unapproved articles are subject to a disclaimer.

The sensitivity and specificity of diagnostic tests are based on Bayes Theorem and defined as "measures for assessing the results of diagnostic and screening tests. Sensitivity represents the proportion of truly diseased persons in a screened population who are identified as being diseased by the test. It is a measure of the probability of correctly diagnosing a condition. Specificity is the proportion of truly nondiseased persons who are so identified by the screening test. It is a measure of the probability of correctly identifying a nondiseased person. (From Last, Dictionary of Epidemiology, 2d ed)."[1]

Successful application of sensitivity and specificity is an important part of practicing evidence-based medicine.

Contents

Calculations

Two-by-two table for a diagnostic test
Disease
Present Absent
Test result Positive Cell A Cell BTotal with a positive test
Negative Cell C Cell DTotal with a negative test
Total with disease Total without disease

Sensitivity and specificity

\mbox{Sensitivity of a test} =\left (\frac{\mbox{Total with a positive test}}{\mbox{Total }without\mbox{ disease}}\right ) = \left (\frac{\mbox{Cell A}}{\mbox{Cell A} + \mbox{Cell C}}\right )
\mbox{Specificity of a test}=\left (\frac{\mbox{Total with a negative test}}{\mbox{Total }without\mbox{ disease}}\right ) = \left (\frac{\mbox{Cell D}}{\mbox{Cell B} + \mbox{Cell D}}\right )

Predictive value of tests

The predictive values of diagnostic tests are defined as "in screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test."[2]

\mbox{Positive predictive value}=\left (\frac{\mbox{Total }with\mbox{ disease and a positive test}}{\mbox{Total with a positive test}}\right ) = \left (\frac{\mbox{Cell A}}{\mbox{Cell A} + \mbox{Cell B}}\right )
\mbox{Negative predictive value}=\left (\frac{\mbox{Total }without\mbox{ disease and a negative test}}{\mbox{Total with a negative test}}\right ) = \left (\frac{\mbox{Cell D}}{\mbox{Cell C} + \mbox{Cell D}}\right )

Threats to validity of calculations

Various biases incurred during the study and analysis of a diagnostic tests can affect the validity of the calculations. An example is spectrum bias.

Poorly designed studies may overestimate the accuracy of a diagnostic test.[3]

References

  1. ↑ National Library of Mediicne. Sensitivity and specificity. Retrieved on 2007-12-09.
  2. ↑ National Library of Mediicne. Predictive value of tests. Retrieved on 2007-12-09.
  3. ↑ Lijmer JG, Mol BW, Heisterkamp S, et al (September 1999). "Empirical evidence of design-related bias in studies of diagnostic tests". JAMA 282 (11): 1061–6. PMID 10493205.
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