Metaheuristic-Algorithm-Assisted Unified Multi-Stage Optimal Trial Design

Music City Center 

Multi-stage adaptive designs represent a significant advancement to facilitate efficient resource allocation and protect patients from ineffective therapies. However, sample-size-minimization designs, like Simon's two-stage, are typically restricted to up to three stages due to computational complexity, while power-maximization designs, like BOP2 design, sacrifice interim cohort size optimization. To address these challenges, we propose generalized multi-stage optimal designs for Phase II trials. Our framework integrates both design classes through a unified objective function, transforming optimization into a coherent minimization task. To overcome computational bottlenecks, we leverage advanced optimization techniques and introduce PSO-GO, a practical variant of particle swarm optimization (PSO) tailored for combinatorial design space optimization, substantially enhancing computational efficiency and scalability. Simulations and a real example demonstrate that the new framework provides robust and efficient design solutions.

Simon's optimal design

BOP2

Cancer

Multi-stage

PSO

Trial design