In bioequivalence design, power analyses dictate how much data must be collected to detect the absence of clinically important effects. Power is computed as a tail probability in the sampling distribution of the pertinent test statistics. When these test statistics cannot be constructed from pivotal quantities, their sampling distributions are approximated via repetitive, time-intensive computer simulation. We propose a novel simulation-based method to quickly approximate the power curve for many such bioequivalence tests by efficiently exploring segments (as opposed to the entirety) of the relevant sampling distributions. Despite not estimating the entire sampling distribution, this approach prompts unbiased sample size recommendations. We illustrate this method using two-group bioequivalence tests with unequal variances and overview its broader applicability in clinical design. All methods proposed in this work can be implemented using the developed dent package in R.

翻译：在生物等效性试验设计中，效能分析决定了为检测出临床上重要效应的缺失所需收集的数据量。效能计算为相关检验统计量抽样分布的尾部概率。当这些检验统计量无法通过枢轴量构建时，其抽样分布需通过重复且耗时的高强度计算机模拟进行近似。本文提出一种新型模拟方法，通过高效探索相关抽样分布的区间（而非整体），快速逼近多种此类生物等效性检验的效能曲线。尽管不估计整个抽样分布，该方法仍能提供无偏的样本量推荐。我们通过两组不等方差的生物等效性检验实例说明该方法的有效性，并概述其在临床设计中的广泛适用性。本文提出的所有方法均可通过开发的R语言dent软件包实现。