Accounting for effect size uncertainty in sample size determination: From simple cases to hierarchical linear models for multi-regional clinical trials

Accurate sample size determination (SSD) is critical in clinical trial design, yet traditional methods that assume a fixed effect size overlooks estimation uncertainty, risking under- or over-powered studies. The assurance-based SSD approach, developed in earlier research, addresses this limitation by integrating statistical power over possible values of the effect size, weighted by their likelihood. Building on this foundation, we extend the framework to multi-regional clinical trials (MRCTs) using a hierarchical linear model (HLM) that can capture both between- and within-region variability in treatment effects. Analytical derivations and numerical studies show that assurance-based sample sizes are consistently larger than those from traditional approaches. Also, under high uncertainty, assurance-based sample sizes may not be defined, which serves as a valuable signal that the available evidence is insufficient to justify proceeding to confirmatory trials. These results demonstrate that the assurance approach provides a more reliable and transparent criterion for determining feasible sample sizes, highlighting its importance as a principled methodology, particularly in MRCTs where between-region variability further accentuates uncertainty in effect sizes.