Sensitivity and specificity: Difference between revisions

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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)."<ref name="MeSH_SnSp">{{cite web |url=http://www.nlm.nih.gov/cgi/mesh/2007/MB_cgi?term=Sensitivity+and+Specificity |title=Sensitivity and specificity |accessdate=2007-12-09 |author=National Library of Mediicne |authorlink= |coauthors= |date= |format= |work= |publisher= |pages= |language= |archiveurl= |archivedate= |quote=}}</ref>
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)."<ref name="MeSH_SnSp">{{cite web |url=http://www.nlm.nih.gov/cgi/mesh/2007/MB_cgi?term=Sensitivity+and+Specificity |title=Sensitivity and specificity |accessdate=2007-12-09 |author=National Library of Mediicne |authorlink= |coauthors= |date= |format= |work= |publisher= |pages= |language= |archiveurl= |archivedate= |quote=}}</ref>


==Calculations==
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|+ Two-by-two table for a diagnostic test
|+ Two-by-two table for a diagnostic test

Revision as of 07:07, 9 December 2007

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]

Calculations

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

Sensitivity and specificity

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]

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.