Hello, I'm Tony O'Hagan.
Welcome to the third
talk in my series,
Bayesian Methods in
This talk is entitled Uncertainty
in Health Economic Evaluation.
You remember the outline
of the course as a whole.
The first two talks were about
Bayesian methods in general,
and you should have
heard and understand
those in order to proceed
now to the third talk
in which we begin a discussion
of how Bayesian methods
are used in health economics.
This talk is about uncertainty
and discusses the ways
in which uncertainty influences our
judgment about cost-effectiveness.
Then the next talk, number 4,
deals with the most important topic
of probabilistic sensitivity analysis
in the fifth talk
where we discuss how we formulate the inputs
uncertainties that go into that analysis.
Here's the road map
for this to talk.
We begin by discussing the basic ideas
and principles of cost-effectiveness,
and how treatments can be compared on the
basis of both their costs and their efficacy.
But at that point, it's as if we knew
all the parameters that we need to know.
In practice, we don't know them.
We're uncertain about them.
Then we discuss uncertainty
and how we make decisions about cost-effectiveness
in the presence of uncertainty.
Statistical methods are used in
health economics in a number of ways,
and one of these is
to analyse data
in which both costs and
efficacy have been observed,
typically in a clinical trial designed
for both those things to be observed.
That's a relatively small part of what
health economics is really about,
because such trials
very rarely give you
all the answers you need to
In practice health
always work on the basis
of economic modelling,
and this talk ends with
a very brief introduction
to economic models,
and then the remaining
two talks are all
about economic modelling and
uncertainty in those models.