Bayesian Multi-stage Optimal Design for Phase II Studies with Particle Swarm Optimization

Music City Center 

Phase II clinical trials are pivotal in experimental treatments. Despite an average cost of \$21 million per trial, only one-third of drugs succeed in Phase II. Multi-stage adaptive designs represent a significant advancement. However, current approaches face major limitations: sample-size-minimization designs are typically restricted to three stages due to computational complexity, while power-maximization designs frequently sacrifice interim cohort size optimization, resulting in sub-optimal trial configurations. 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 introduce PSO-GO, a practical variant of particle swarm optimization (PSO) tailored for combinatorial design space optimization. Using this framework, we developthe G-BOP2 design, which incorporates multi-faceted enhancements to the Bayesian Optimal Phase II (BOP2) design. Simulations and real example demonstrate that G-BOP2 provides robust and efficient design solutions.

Unified optimal design

Unified objective function

Global optimality

PSO

G-BOP2 

Biopharmaceutical Section