Medical statistics plays an essential role in all stages of a quantitative health care research project from design through to analysis and interpretation. This intensive course covers the essential principles and methods required. Emphasis is on study design, appropriate analysis, and interpretation of results. The underlying concepts of statistical analysis as well as basic and some more advanced analysis techniques are covered. Sessions include lectures and practical work, both computer based (using Stata) and using small workshops for discussion. The course has been running for more than 30 years and has earned an international reputation.

Please note that the Early Bird Rates are only available until the 2nd May 2025.

This residential 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 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), The BUGS Book (BB) and Evidence Synthesis for Decision Making in Healthcare (ESDM).

Please note that all fees include tuition and access to the course material (slides, R/BUGS code/scripts for the practicals, relevant papers etc).

We are excited to announce the R for Health Technology Assessment (HTA) workshop that will be held on Friday 6th June, Monday 9th & Tuesday 10th June 2025.

Friday 6th June will be an in-person day-long, hybrid event hosted at Queen's University Belfast (QUB), Ireland, while the other days will be online only. Our programme will be announced in April. The overall goal is to present interesting and enlightening presentations on the use of R that will engage an audience of those working in the field of health technology assessment and related analysis. Sessions may cover some or all of the following:

    New methods and applications for economic modelling using R

    Efficient modelling for economic evaluation using dedicated R packages

    Improving modelling for HTA using R – Lessons from industry & academia

    Teaching economic evaluation and HTA using R

Abstract

A wide range of fields, from clinical medicine to social science, seek to use empirical data to learn how different factors affect the world. Making credible causal claims can be very difficult, particularly using observational rather than experimental data. Causal inference provides a framework to clarify and assess the assumptions on which causal interpretation depends and develop statistical tools which can be implemented in practice. 

This course covers the fundamental developments in causal inference methods and gives practical explanations about how to apply these methods to real research questions. The course will cover: potential outcomes, target trials, propensity score, and instrumental variable analysis. Each approach will be explained within the causal inference framework, along with the recommended sensitivity analyses, validation, and specification tests to assess the plausibility of the analysis, where applicable.