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Hi, I'm Tony O'Hagan.
Welcome to the fourth
talk in my series,
Bayesian Methods in
This talk is entitled Probabilistic
Let's quickly review one more time
the way the whole course looks.
The first two talks dealt with
Bayesian approach to statistical
inference in a general way.
Then in the third talk,
we moved on to explaining how
Bayesian methods are applied
in the area of health
and in particular, we looked at
the uncertainties in health
This talk is about probabilistic
which is the most important way that
health economists use Bayesian methods.
The final talk will deal with another aspect
of probabilistic sensitivity analysis,
that of formulating
Here's the road map
for this fourth talk,
we begin by discussing
which is the context in which probabilistic
sensitivity analysis takes place.
We then move on to PSA itself,
and identify three key
steps in doing that.
Now, the fifth talk is entirely
devoted to one of those steps,
that of quantifying
The remainder of this talk deals
with the other two important steps.
One is computing
how we express
and how they influence
The context, as I explained in
the previous talk, number 3,
one way that Bayesian
methods are used in
the health economic
evaluation is to analyse
the outcomes of clinical trials in which
we measure both costs and efficacy.
But as I also explained then,
this is rarely the case that health
economists find themselves in.
A single clinical trial
will rarely provide
all the evidence that you need
for cost-effectiveness study.
There are a number
of reasons for this.
First of all, let's emphasise
the single clinical trial.
Very often there is
more than one trial,
and we need to be able to combine
the information from several things.
But in that case,
almost surely some of them
will not deal with costs,
and were only
looking at efficacy.
Indeed, the common situation is where clinical
trials have only been evaluating efficacy.
Even where we have a trial in which
costs might be measured as well
the conditions of those trials are rarely what
we need for deciding on cost-effectiveness
because in a clinical
we invariably are looking
at limited follow-up,
and that may be very important in clinical
diseases which take some time to evolve,
we're invariably looking
at restricted enrollment,
which ensures that you get a patient group for
which you might be able to observe an effect,
whereas in the real world,
we might be wanting to use this
treatment in a wider population group.
We have in clinical trials an
unrealistic level of compliance.
The patients typically will comply
with the taking of the medicines
far more so than they would
out in their own homes.
We have the wrong
comparators very often.
We are looking at comparisons against
placebos or against treatments
that are not the ones
that we're really
comparing this treatment when
it comes to cost-effectiveness.
Finally, we very often have the
wrong outcomes in a clinical trial.
People run clinical trials in
order to demonstrate some effect,
but it may not be the effect that in economic
terms would be most valuable to see.
There are many ways in
which a clinical trial
fails to live up to what
a health economist needs
in order to evaluate
the treatment under
against the relevant
Instead, health economists rely
on a process of modelling.
We look at a very simple
example in order to