Next Speaker: Steve Chick is a Professor of Technology and Operations Management at INSEAD. He was named Academic Director of INSEAD's Healthcare Management Initiative and the Novartis Chair of Healthcare Management in 2008. He received his MS and PhD from the University of California at Berkeley in Industrial Engineering and Operations Research and his BS in Mathematics from Stanford University. Prior to joining INSEAD in 2001, he was a faculty member at the University of Michigan’s Industrial and Operations Engineering department and had worked for five years in the automotive and software industries. His research brings together operations management, simulation and statistical decision-making tools to help improve health care operations and new health technology assessment. He has held all elected officer positions of the INFORMS Simulation Society and has been active with editorial responsibilities, is a past president of the INFORMS Health Applications Society, and is an INFORMS Fellow.
Seminar Title: Effective Health Technologies Faster? Value-Based, Response Adaptive Learning in Clinical Trials
Date and Time: Friday, September 26th, at 1:00-2:00 PM EST
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 look at basic trade-offs in a fully sequential two-arm trial design, to balance the costs of collecting more trial data with the expected opportunity costs averted by making decisions with better information. We then explore three “RE”s motivated by trials in practice: RElevance to health system processes (e.g., clinical trials in public funded systems like the UK), REinforce the base model (with extensions to multi-arm trials and precision medicine), and REward (account for financial incentives like pricing and conditional approval in the drug development supply chain).
Registration Zoom Link HERE