Bayes Theorem with Odds and LR's: Teaching Prototype
2018
In this video, Professor Myriam Hunink walks through an analytic approach to probability revision using the Odds-LR form of Bayes. Students consider how an initial probability or belief is influenced by new diagnostic information through the use of Bayes' theorem.
Access the video. Bayes Theorem with Odds and LR's: Teaching Prototype (~9 min)
Using the example of 100 patients with a tick bite and suspected Lyme disease, Professor Hunink visually maps out the prior odds of disease and reviews the use of likelihood ratios to describe test performance. In this case, assuming a dichotomous test, we are concerned about two likelihood ratios - the likelihood ratio for a positive test (LR+) and the likelihood ratio for a negative test (LR-). She derives the equation, posterior odds = prior odds x LR, otherwise known as the “Odds-LR form of Bayes”. The video concludes with reflection on the influence of the likelihood ratio positive and negative on the posterior or post-test odds of disease.
This video is one of a series developed by Professor Myriam Hunink during an immersion residency at the Center for Health Decision Science (CHDS) Media Hub. The video series reflect experiments to augment brick and mortar teaching with multimedia materials that emphasize visualization of basic concepts.
Bayes Theorem with Odds and LR's: Teaching Prototype
Source:
Bayes Theorem with Odds and LR's. Teaching Pack: Teaching Prototypes for Decision Analysis. Center for Health Decision Science, Harvard T.H. Chan School of Public Health 2018. https://vimeo.com/236607957/fac8283e7c