0:00
Hello, I'm Tony O'Hagan.
Welcome to the second
talk in my series,
Bayesian Methods in
Health Economics.
This talk is entitled
Prior Distributions.
0:14
Let's recap on the outline
of the course as a whole.
The first talk was on
Bayesian principles,
and from that you should
have learned about
the basic ideas of the Bayesian
approach to statistics,
and how it differs particularly
from the frequentist approach
which is more familiar
to most people.
In this second talk, we'll
deal with prior distributions,
which are something unique that
goes into a Bayesian analysis.
It's very important to
think about those properly.
These first two talks
make up the section
that's about Bayesian
methods in general.
Then we move on to the
particulars of how we use
Bayesian methods in health
economic evaluation.
The first of those, talk three,
is about uncertainty in
health economic evaluation.
Then we move on to dealing with specifically
probabilistic sensitivity analysis,
which is a way of coping with and
quantifying those uncertainties.
Finally, the fifth talk is about formulating
input uncertainties to go into that analysis.
Those last three parts
as a whole deal with
the whole ideas of
Bayesian methods
in health economic evaluation.
1:20
Here is the plan of this talk.
We will begin by briefly reviewing
the idea of prior information,
and then move on to the topic
of weak prior distributions.
Here, the idea is to represent
the fact that we have
minimal prior
information relative
to what we're going
to get from the data.
Having dealt with that case,
we move on to the
contrary situation
where we do have useful prior information
that will contribute to the analysis,
and we want to formulate
informative priors to represent it.
The key way of doing that is through
the process called elicitation.
Then there'll be a case
study of using elicitation.
Which leads us on to the situation
where we have many parameters
and we need to structure the
prior information appropriately.
That's the idea of
hierarchical modelling.
Prior information
comprises everything