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.

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.