BSU Seminar by Prof Stephen Chick, INSEAD скачать в хорошем качестве

BSU Seminar by Prof Stephen Chick, INSEAD

Title: "Effective Health Technologies Faster? Value-Based, Response Adaptive Learning in Clinical Trials" Speaker: Professor Stephen Chick, INSEAD Abstract: 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 between incentives at different steps of the health innovation pipeline, clinical effectiveness on the one hand and cost-effectiveness on the other, may lead to suboptimal decisions? We introduce and explore a stream of work that seeks to improve the allocation of resources to clinical trials in a way that balances health value for money for treatments that are ultimately approved. The stream uses work from Bayesian sequential optimal learning and from game theory. We first explore work with this mindset from the area of precision medicine trials, where the goal is to learn quickly which of multiple treatments is most effective for patients, and when there might be insights as to which covariate values are prognostic or might be predictive with respect to some of the treatments. Time permitting, and certainly in overflow discussions, we then explore how to balance how much clinical trial expense is `worth it’ in terms of health economic benefit that might be gained based on technology adoption decisions that are based on those trials. This is part of a broader-based value-adaptive trial framework that also has applications UK-NIHR funded clinical trials (including retrospective looks at the ProFHER trial, the CACTUS trial, and the HERO trial) and that explores implications for conditional approval schemes (motivated by the UK Cancer Drugs Fund). This was a hybrid seminar organised by the MRC Biostatistics Unit on Tuesday 30th September 2025.