Calculate the probability of an outcome based on clinical conditions and diagnostic tests
With Bayes' Theorem, the pretest probability is likelihood of an event or outcome based on demographic, prognostic, and clinical factors prior to diagnostic testing. In order to calculate the post-test probability, one must know the likelihood of having either a positive or negative test result in the current clinical situation. The likelihood ratio of a positive diagnostic finding is calculated with the following formula.
In other diagnostic instances with Bayes' Theorem, negative test results are the focus of the diagnostic study or treatment regimen. The likelihood ratio of a negative diagnostic test result can be found with the following formula.
Most diagnostic tests provide an outcome that is dichotomous for "positive" or "negative" for a given disease state or outcome/event. However, if the diagnostic test uses a continuous scale to produce diagnostic results, then researchers can generate the positive likelihood ratio for each different cut-off along the numerical continuum. There are three steps to calculating these likelihood ratios according to Bayes' Theorem:
1. Convert the pretest probability to pretest odds with the first formula below.
2. Multiply the pretest odds by the likelihood ratio to get the post-test odds (second formula below).
3. Convert the post-test odds back into a probability with the third formula below.
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