This summer school aims at providing an introduction to Bayesian analysis and Markov Chain Monte Carlo (MCMC) methods using R and MCMC sampling software (such as OpenBUGS and JAGS), as applied to cost-effectiveness analysis and typical models used in health economic evaluations. We will also focus on more recent methods for Probabilistic Sensitivity Analysis including Value of Information calculations. As such, it is intended for health economists, statisticians, and decision modellers interested in the practice of Bayesian modelling and will be based on a mixture of lectures and computer practicals. The emphasis will be on examples of applied analysis: software and code to carry out the analyses will be provided.

Participants are encouraged to bring their own laptops for the practicals. We shall assume a basic knowledge of standard methods in health economics and some familiarity with a range of probability distributions, regression analysis, Markov models and random-effects meta-analysis. However, statistical concepts are reviewed in the context of applied health economic evaluations in the lectures. Lectures and practicals are based on Bayesian Methods in Health Economics (BMHE), Bayesian Cost-Effectiveness Analysis with the R package BCEA (BCEA), The BUGS Book (BB) and Evidence Synthesis for Decision Making in Healthcare (ESDM).

This summer school essentially is made of two halves — the first main topic is Bayesian modelling; the second is its application to health-economic evaluation. These two will be intertwined throughout — we will switch back and forth to the description of the methods and their application to data on cost-effectiveness of (mainly, but not exclusively) pharmaceutical interventions.

In any case, the main learning objective of this summer school is for you to be able to perform a Bayesian analysis (specifically on health care data and with the objective of a full economic evaluation). We will be formal in the exposition of the technical concepts, while not fixating with proofs and theorems (but rather trying to provide the rationale and the intuition behind the use of the various methods).

In order to achieve this goal, much of the summer school will concentrate on practical skills, such as programming and doing data analysis in R and BUGS. Both pieces of software are publicly available and you can install them on your machine. You probably have used R in your previous work/studies. In any case, you will be given a lot of material to make up for any lack of knowledge. We expect you to go through the exercises and try to improve your computational skills throughout the summer school. As for BUGS, you probably have not seen it before — but again, you will be provided with a wealth of material and so, by the end of the week, you should be able to write, run and debug BUGS code proficiently!

In 2022, the summer school was hosted in the main campus at the University of Lausanne.

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