Standard cost-effectiveness analysis tells you how much health an intervention buys, but says nothing about who gains and who loses. Join us for a 90-minute interactive tutorial with Ahwaz Akhtar (The George Washington University) who'll walk us through Distributional Cost-Effectiveness Analysis (DCEA) - a framework for bringing equity directly into health economic evaluation.

Using a case study, participants will learn to model how health gains and costs are distributed across population subgroups, apply fairness adjustments, and evaluate competing interventions using inequality indices and social welfare tradeoffs, all through live-coded R examples. The session combines short presentations, guided coding walkthroughs, and breakout discussion, and is designed for early-career researchers and practitioners in health data science who are already comfortable with basic CEA/QALY concepts but new to distributional methods.

By the end, you'll be able to:

• Explain what standard CEA misses and what DCEA adds

• Work through the two-stage DCEA framework: modeling social distributions, then evaluating them

• Build the core building blocks in R: subgroup health distributions, fairness adjustments, inequality indices, and equity-efficiency tradeoff curves

• Interpret dominance rankings and social welfare comparisons between interventions

• Identify how DCEA could apply to your own research

This session is a partnership between DSxHE's Statistical Methods and Data Diversity Themes, in collaboration with Cancer Research UK.

Who it's for:  No prior experience with distributional or equity-weighted evaluation required - just basic familiarity with R/Python, standard CEA concepts and a lot of enthusiam for data diversity! We'll send you a very small amount of pre-reading beforehand.

About the Data Diversity Theme

Health datasets often don’t reflect the diversity of the populations they aim to serve. This lack of representativeness can limit the generalisability of research, reduce the effectiveness of new tools, and risk widening health inequalities.

A partnership between Data Science for Health Equity and Cancer Research UK, the Data Diversity Theme brings together researchers, clinicians, funders, and patient advocates to co-create practical ways of embedding diversity across the research lifecycle. It is co-led by Dr Toral Gathani (University of Oxford) and Dr Brieuc Lehmann (UCL).

Any questions?

Please direct any queries to info@datascienceforhealthequity.com.