The UCL Centre for Data Science is pleased to announce a six-day Data Science Summer School for professionals and graduate students seeking a comprehensive introduction to the fundamentals of data science.

We are excited to announce the R for Health Technology Assessment (HTA) workshop that will be held on Monday 22nd June, Tuesday 23rd June & Wednesday 24th June 2026.

Monday 22nd June will be an in-person day-long, hybrid event hosted at University of Exeter, 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

Applied health and population health researchers are often asked to answer causal questions using empirical data: does an exposure affect later health, does a treatment work outside a trial, or does a policy or service change improve outcomes? These questions are difficult to answer using observational data alone, because associations may reflect confounding, selection bias, missing data, measurement error or inappropriate adjustment.

This three-day in-person course provides practical training in causal inference for researchers working with cohort studies, electronic health records, routinely collected data, trials with observational follow-up, administrative data and other population health datasets. The course introduces key causal inference frameworks and methods, including potential outcomes, directed acyclic graphs, target trial emulation, propensity score methods, regression adjustment, instrumental variable analysis and sensitivity analyses.

Each approach will be explained using applied examples, with emphasis on defining clear causal questions, making assumptions explicit, choosing appropriate analytical strategies, assessing robustness and interpreting results cautiously. Computer practicals will be provided in both R and Stata and will focus on implementing methods in realistic applied research settings.