Thursday, June 20
Monday, June 24, 2024
Lecturer(s): Prof. Gianluca Baio, Dr. Nathan Green and Anna Heath Location: Lausanne ECTS: 3
The course 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.
Local Coordinator: Joachim Marti
Registration (see course list)