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Bayesian methods in health economics: Bayesian principles 1

Part 1 of 5
Published on February 16, 2009 Reviewed on March 30, 2022   42 min

A selection of talks on Methods

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0:00
Hello. I'm Tony O'Hagan. This is the first of five talks in my series, Bayesian Methods in Health Economics, and this one is entitled Bayesian Principles.
0:13
Before we get started, I thought it'd be interesting to look at this slide. This graph shows the increase in publications in the area of Bayesian statistics published in Medicine. The reason why we're doing this course is because there has been a phenomenal growth in the interest in Bayesian methods in all kinds of areas, but particularly in medicine, as you can see from this graph. This shows a proportion of publications, not actual numbers, but the amount per 100000, that actually list Bayesian methods. You can see there's a growth in not just numbers, but in the proportion that's showing an interest in Bayesian methods.
0:52
Here's an outline of the course as a whole. The first two parts out of the five deal with a general introduction to Bayesian methods. Part one, which is this one, is on the basic principles behind Bayesian methods and how and why they work. The second part, entitled Prior distributions, concerns how we formulate the uncertainty on parameters that you need to put in to start the process. You'll understand what that means after we've done this particular part. Then the next three parts deal with Bayesian methods in the health economic situation. The first part is outlining what it is about uncertainty in health economic evaluation that we need to quantify in order to be able to do the job well. Then, in part four, the crucial area of Probabilistic sensitivity analysis in health economics. This is to do with economic modelling and how you quantify uncertainties in that. We'll deal with that in part four. And in part five, an echo of part two where we put the uncertainty into the parameters that go into the health economic model.
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Bayesian methods in health economics: Bayesian principles 1

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