BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:BSU Seminar: "\;Effective Health Technologies Faster? Value-Ba sed\, Response Adaptive Learning in Clinical Trials"\; - Professor Ste phen Chick\, INSEAD DTSTART:20250930T130000Z DTEND:20250930T140000Z UID:TALK234904@talks.cam.ac.uk CONTACT:Alison Quenault DESCRIPTION:Clinical trials are used to evaluate the health benefit of new health technologies\, such as pharmaceuticals\, but are quite costly and therefore have been the subject of much study. Health technology adoption decisions are often made based on not only health benefit\, but the costs of drugs and treatment processes. Is it possible that this mismatch betwee n incentives at different steps of the health innovation pipeline\, clinic al effectiveness on the one hand and cost-effectiveness on the other\, may lead to suboptimal decisions? We introduce and explore a stream of work t hat seeks to improve the allocation of resources to clinical trials in a w ay that balances health value\n for money for treatments that are ultimate ly approved. The stream uses work from Bayesian sequential optimal learnin g and from game theory. We first look at basic trade-offs in a simple two- arm fully sequential trial design\, to balance the costs of collecting mor e trial data with the expected opportunity costs averted by making decisio ns with better information. We then explore how the theory can apply to UK -NIHR funded clinical trials (including retrospective looks at the ProFHER trial\, the CACTUS trial\, and the HERO trial)\, and overview extensions that allow the framework to apply to multiarm trials\, precision medicine trials\, and explore implications for conditional approval schemes (motiva ted by the UK Cancer Drugs Fund). LOCATION:Large Seminar Room\, East Forvie Building\, Forvie Site Robinson Way Cambridge CB2 0SR. END:VEVENT END:VCALENDAR