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 30th April 2026.

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).

This year’s Annual Lecture hosted by the Institute for Mathematical and Statistical Sciences (IMSS) will focus on Computational Statistics and Machine Learning (CSML). We are delighted to host Dr. Lester Mackey (Microsoft Research New England and Stanford University), whose research interests include statistical machine learning and approximate inference, with a focus on scalable learning algorithms. The lecture also features invited talks by Prof. Po-Ling Loh (University of Cambridge), Paula Cordero Encinar (Imperial College London), and Dr. Alessandro Barp (UCL), who will present work on robust statistics, generative modelling, and statistical machine learning. 

The London Meeting in Computational Statistics is a two-day workshop focused on recent advances in Monte Carlo methods, gradient flows, simulation-based inference, and variational inference, bringing together researchers working at the forefront of computational statistics. The workshop runs alongside the UCL Institute of Mathematics and Statistical Sciences (IMSS) Annual Lecture. Confirmed speakers include Prof. Arnaud Doucet (University of Oxford), Dr. Dennis Prangle (Lancaster University), Dr. Anna Korba (ENSAE), Dr. Mathieu Gerber (University of Bristol), Dr. Gilles Louppe (University of Liège), Dr. Heishiro Kanagawa (Newcastle University), and Arina Odnoblyudova (UCL).

Please note that there are no single-day tickets available; booking comprises the entire two-day workshop.

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.