Understanding diagnostic tests 2: likelihood ratios, pre‐ and post‐test probabilities and their use in clinical practice

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Abstract
The sensitivity and specificity of a test cannot be used to estimate probability of disease in individual patients. They can, however, be combined into a single measure called the likelihood ratio which is, clinically, more useful than sensitivity or specificity. Likelihood ratios provide a summary of how many times more (or less) likely patients with a disease are to have a particular result than patients without the disease. Using the principles of the Bayes theorem, likelihood ratios can be used in conjunction with pre‐test probability of disease to estimate an individual's post‐test probability of disease, that is his or her chance of having disease once the result of a test is known. The Fagan's nomogram is a graphical tool which, in routine clinical practice, allows one to combine the likelihood ratio of a test with a patient's pre‐test probability of disease to estimate post‐test probability.Conclusion: Likelihood ratios summarize information about a diagnostic test by combining sensitivity and specificity. The Fagan's nomogram is a useful and convenient graphical tool that allows likelihood ratios to be used in conjunction with a patient's pre‐test probability of disease to estimate the post‐test probability of disease.