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1. Bayesian methods in health economics: Bayesian principles 1
- Prof. Anthony O'Hagan
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2. Bayesian methods in health economics: prior distributions 2
- Prof. Anthony O'Hagan
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3. Bayesian methods in health economics: uncertainty in health economic evaluation 3
- Prof. Anthony O'Hagan
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4. Bayesian methods in health economics: probabilistic sensitivity analysis 4
- Prof. Anthony O'Hagan
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5. Bayesian methods in health economics: formulating input uncertainty 5
- Prof. Anthony O'Hagan
Printable Handouts
Navigable Slide Index
- Introduction
- Interest in Bayesian methods
- Course outline
- Talk outline
- The Bayesian paradigm
- The Bayesian method (1)
- The Bayesian method (2)
- The Bayesian method (3)
- Example: prior distribution
- Example: the first year’s data
- Example: the first year's triplot
- Example: the second year's data
- Example: the second year's triplot
- First Bayes
- Formulating prior distributions
- Computing tools
- Bayesian vs. frequentist
- Statistical inference
- The nature of probability
- The nature of parameters
- The use of prior information
- The nature of inferences
- The nature of inferences: posterior density plot
- The nature of inferences: contour plot
- Bayes is better!
- Concluding remarks
Topics Covered
- Bayesian statistics
- Frequentist statistics
- Bayesian vs. frequentist approach
- Prior distribution
- Triplots
- Posterior distribution
- Statistical computational tools
- Statistical inference
- The nature of parameters
- The use of prior information
- The nature of probability
Talk Citation
O'Hagan, A. (2022, March 30). Bayesian methods in health economics: Bayesian principles 1 [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved October 12, 2024, from https://doi.org/10.69645/FMLC1485.Export Citation (RIS)
Publication History
Financial Disclosures
- Tony O'Hagan acts as a consultant providing training and advice on the use of SHELF.
Bayesian methods in health economics: Bayesian principles 1
A selection of talks on Methods
Transcript
Please wait while the transcript is being prepared...
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.