Aims - This module aims to provide an introduction to Bayesian analysis and Markov Chain Monte Carlo (MCMC) methods using R and MCMC sampling software (such as BUGS or JAGS), as applied to cost-effectiveness analysis and the typical models used in health economic evaluations. Emphasis will be placed on the practical side of Bayesian inference. It is primarily intended for third and fourth year undergraduate students and taught postgraduate students registered on the degree programmes offered by the Department of Statistical Science, or jointly with other departments. The academic prerequisite for such students is STAT0008.

Objectives - On successful completion of the module, a student should be able to: understand the basic concepts of Bayesian analysis, and design, build, run and interpret the results of a Bayesian model, with specific application to health economic problems. These skills are widely transferrable to a variety of fields and applications. Specific topics include an introduction to health economics, a review of a range of probability distributions, regression analysis, Markov models and random-effects meta-analysis.

Applications - This module has applications in medicine and studies on public health, epidemiology and health services research.

Content - Introduction to health economic evaluations; Introduction to Bayesian inference; Introduction to MCMC in BUGS/JAGS; Analysis of cost and cost-utility data; Statistical cost-effectiveness analysis; Probabilistic Sensitivity Analysis (PSA); Evidence synthesis and hierarchical models; Decision-analytic and Markov models. The practical sessions are based on a combination of R and BUGS and topics include: Monte Carlo estimation in BUGS; MCMC estimation in BUGS; Cost-effectiveness analysis with individual level data; Introduction to R and cost-effectiveness analysis using the R package BCEA; Health economic evaluation and PSA with R/BUGS/BCEA; Advanced topics in PSA in R using BCEA; Evidence synthesis (1): decision models; Evidence synthesis (2): network meta-analysis; Markov models in health economics.

Key texts - Available from ReadingLists@UCL.