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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.
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.
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.