Optimal utility-based design of oncology clinical development programmes

Cancer clinical trials are currently designed one at a time, but this project will create a mathematical framework to plan entire sequences of trials in advance, optimising for patient benefit. The problem is that developing a new cancer drug typically requires multiple clinical trials—early-phase tests for safety, later-phase tests for efficacy—but these are usually planned in isolation. This fragmented approach wastes time, money, and patient goodwill. It also misses opportunities to adapt the programme as early results come in. The gap is a practical, quantitative method to compare entire development strategies, not just individual trials. If this research succeeds, it will give pharmaceutical companies and academic trialists a tool to calculate which sequence of trial designs—and which endpoints—maximises the probability of success while prioritising outcomes that matter to patients, such as survival or quality of life. The framework uses probabilistic dynamic programming, meaning it can update recommendations as new data accumulate. The immediate impact is on clinical development efficiency: fewer failed late-stage trials, faster access to effective treatments, and better allocation of limited trial participants. This is applied methodology, not fundamental science—it directly targets how decisions are made in drug development.