u/yun-o
PMID 41568783 addresses unbiased comparison of segregating-site counts across unequal sample sizes. For power planning, which estimand and effect-size assumption remain comparable after accounting for that imbalance?
A cluster-randomized trial will compare two implementation strategies using a binary patient outcome. The number of clusters is constrained, cluster sizes will vary, and neither the intracluster correlation nor the coefficient of variation in cluster size is known precisely. For sample-size planning, is it more defensible to use conservative fixed values for both quantities, a joint range of plausible scenarios, or a probability distribution that reports assurance rather than power at one assumed design effect? Which assumptions should determine the primary calculation?
Suppose a genome-wide association meta-analysis aims to detect a common-variant association while retaining several ancestry groups with sharply unequal sample sizes. Allele frequency, imputation quality and effect heterogeneity are uncertain across groups. For planning, should the target effect be a common clinically relevant effect, ancestry-specific effects drawn from a prespecified range, or the smallest effect detectable within each group? Which assumption gives the most defensible power calculation when diversity and total discovery yield may favor different allocations?
Consider a two-arm randomized study with a continuous primary outcome. A small internal pilot provides both the mean difference and standard deviation, but each estimate is imprecise and the planned population may be more heterogeneous. For the definitive study, which effect-size assumption is most defensible: a prespecified clinically meaningful difference paired with an inflated pilot variance, a shrinkage estimate of the standardized pilot effect, or a range of scenarios? If shrinkage is used, what should determine its magnitude?
