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).

In forecast evaluation, a scoring rule provides an evaluation metric for probabilistic predictions or forecasts. That is, the scoring rule assigns a numerical score to the forecast by comparing the forecast and the realised observation. A scoring rule is called proper if it has the property that the score is optimised in expectation when the true data distribution is issued as the forecast. This property is considered a necessary condition for a decision-theoretically principled forecast evaluation. The class of proper scoring rules is large and diverse, so that proper scoring rules may evaluate different aspects of the forecast. For example, it has been stated that the aim of probabilistic forecasting should be to maximise the sharpness of the forecast subject to calibration. That is, there should be statistical compatibility between the predictive distribution and the observation while, at the same time, the forecast should provide as much information on the observation as possible. Different proper scoring rules are able to assess these properties to a different degree. On the other hand, if we want the forecast to possess certain properties that are well measured by a certain proper scoring rule, it seems natural to also consider this same scoring rule as a loss function when estimating the forecast. This line of thinking is increasingly being used in machine learning, in particular in applications such as meteorology where there is a long tradition for forecast evaluation with proper scoring rules. We will cover the foundations of forecast evaluation with a focus on proper scoring rules and related evaluation metrics, discuss some recent developments and, in particular, connections to the training of machine learning algorithms.

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