Assurance-Based Sample Size Determination for Multi-Regional Clinical Trials with Binary Endpoints Accounting for Effect Size Uncertainty

While binary endpoints are commonly used in clinical trials, their sample size determination (SSD) presents distinct methodological challenges. In this paper, we address SSD for multi-regional clinical trials (MRCTs) under a hierarchical generalized linear model (HGLM) framework that accommodates both random baseline and random treatment effects. To overcome the limitations of traditional methods that rely on a single, fixed assumed effect size, we employ the concept of assurance. Assurance is defined as the unconditional expected power, obtained by integrating the conventional power function over a prior distribution of the treatment effect, thereby accounting for effect size uncertainty at the design stage. We derive closed-form SSD formulas and analytically compare the power- and assurance-based approaches. Simulation studies using plug-in estimates show that, with high probability, the assurance-based approach yields larger sample sizes than the power-based benchmark.