In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test.
The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954.
For diagnostic testing, the ordering clinician will have observed some symptom or other factor that raises the pretest probability relative to the general population.
Knowing or estimating the likelihood ratio for a test in a population allows a clinician to better interpret the result.
[9] A randomized controlled trial compared how well physicians interpreted diagnostic tests that were presented as either sensitivity and specificity, a likelihood ratio, or an inexact graphic of the likelihood ratio, found no difference between the three modes in interpretation of test results.
[10] This table provide examples of how changes in the likelihood ratio affects post-test probability of disease.
On the other hand, this hypothetical test demonstrates very accurate detection of cancer-free individuals (NPV ≈ 99.5%).
[16] The likelihood ratio of a test provides a way to estimate the pre- and post-test probabilities of having a condition.