Sami N.

I would require independent ancestry-specific estimates for a comparable phenotype and variant, with uncertainty narrow enough to support the proposed lower bound after considering selection bias and population differences. No such evidence has been identified here. A chosen minimum relevant effect can define a conditional power requirement, but cannot establish that smaller effects are implausible. [DELTA2 guidance](https://www.ncbi.nlm.nih.gov/books/NBK549158/) distinguishes important effects from realistic effects supported by evidence; it concerns trials, so applying that distinction here is a planning judgment. Without support for excluding near-zero effects, retain them in sensitivity analyses and state that the detection target applies only above the prespecified effect threshold.

If adequate detection within every ancestry group is required, I would use minimum power over a defensible joint scenario set as the primary constraint. That set should reflect plausible combinations, since combining every marginal extreme can create an implausible worst case. Assurance incorporates parameter uncertainty through prior distributions, as illustrated in [Williamson and colleagues’ cluster-trial study](https://pubmed.ncbi.nlm.nih.gov/39935065/). My recommendation for this ancestry setting is to use assurance as a complementary summary when those distributions are defensible: meeting an average target still permits low power in some scenarios, so it cannot establish the proposed minimum within each group.

Power the ancestry-specific effects needed for the intended cross-group decision, using prespecified ranges for allele frequency, imputation quality, and heterogeneity, because a common effect can hide subgroup shortfalls.

For the definitive trial, I would anchor the primary calculation to a prespecified clinically meaningful mean difference, not the pilot mean difference. Current guidance treats small pilot effects as too unstable for this role. Use external evidence plus the pilot to define a plausible variance range, with the pilot variance inflated through an upper confidence limit or another prespecified method. Report sample sizes across those scenarios rather than presenting one fragile value. If feasible, blinded variance re-estimation in the definitive trial can address remaining heterogeneity uncertainty. Shrinkage of the pilot effect is defensible only when it represents a formal prior or empirical model built from relevant external studies. Its magnitude should follow the estimated between-study variation, similarity of populations and outcomes, and prior-data conflict, not an arbitrary percentage. What decision must the powered estimate support: detecting the smallest worthwhile effect, estimating an effect to specified precision, or making a progression decision?