Institute: ONC | Component: 2 | Unit: 5 | Lecture: d | Slide: 6
Institute:Office of National Coordinator (ONC) Workforce Training Curriculum
Component:The Culture of Health Care
Unit:Evidence-Based Practice
Lecture:Phrasing the clinical question Harm and prognosis
Slide content:Some Other Probability Principles Sum of all probabilities should equal 1 e.g., p[heads] + p[tails] = 0.5 + 0.5 = 1 Bayes theorem in diagnosis Post-test (posterior) probability a function of pre-test (prior) probability and results of test Post-test probability variable increases with positive test and decreases with negative test 6
Slide notes:6 Another principle to consider when talking about probability is that the sum of all probabilities should equal one. For example, with a coin flip, the probability of head or tails is each point-five, which adds up to one. When we calculate the probability of a disease with information from a diagnostic test, we use Bayes [ bayz ] theorem, which is a statistical formula that gives us the post-test probability, sometimes called the posterior probability . In this case, it gives us the post-test probability of a disease being present. Bayes theorem has many uses in addition to medical diagnosis. The post-test probability is a function of both the pre-test probability and the results of the test. Bayes theorem tells us that its important to know what the prior or pre-test probability is because that information is used to calculate a new probability when test results are added. When we calculate the probability of a disease with information from a diagnostic test, we use Bayes [ bayz ] Theorem, which is a statistical formula that gives us the post-test probability, sometimes called the posterior probability. It gives us the post-test probability of, in this case, a disease being present. Bayes [ bayz ] Theorem is also used for things other than medical diagnosis. The post-test probability is a function of both the pre-test probability and the results of the test. Bayes [ bayz ] Theorem tells us that it is important to know what the prior or pretest probability is as that information is used to calculate a new probability when test results are added.