Economic Evaluation and Simulation Modelling in Diabetes
2 Oct 2026
(We anticipate the course running 9:30am-4:30 pm,
but exact times to be confirmed)
An entirely new one-day pre-conference short course will be held on Friday 2 October, providing an overview of key methods in the economic evaluation of diabetes interventions. Drawing on material from an upcoming Oxford University Press volume, the course will combine conceptual teaching with practical demonstrations in Stata and R.
Course content will include:
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Introduction and overview of the economic evaluation of diabetes interventions
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Survival analysis to quantify outcomes and inform model development
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Regression analysis of cost and quality-of-life data
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Development of event-based simulation models (including a detailed overview of the UKPDS Outcomes Model)
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Cost-effectiveness analysis and the reporting of results
Teaching will focus on practical application, offering participants insight into modelling approaches and their use in health economic evaluation.
You can attend the short-course without registering for the Mt Hood conference.
Speakers
Professor Philip Clarke, holds appointments at the University of Oxford and Melbourne. He was instrumental in the development of both versions of the UKPDS Outcomes Model. More recently he has been involved in the development of a comparable Type 1 diabetes simulation model using data from a large diabetes registry in Sweden. He has also been involved with the economic analyses of the major diabetes clinical trials including the UKPDS, FIELD and ADVANCE studies and has collaborated with researchers across Asia.
Associate Professor Aaron Winn, Department of Pharmacy Systems, Outcomes and Policy at the Retzky College of Pharmacy, University of Illinois Chicago. His research focuses on identifying high-value treatments and understanding the patient and provider factors that influence the uptake of new therapies. He has extensive experience working with large claims and electronic medical record datasets, as well as developing mathematical models of disease progression to evaluate comparative effectiveness and healthcare decision-making.
