A clinical endpoint bioequivalence (BE) study is often used to establish bioequivalence (BE) between a locally acting generic drug (T) and an innovator drug (R), which is a double-blind, randomized three-arm (T, R and placebo: P) parallel clinical trial. BE is established if two superiority tests (T vs. P, R vs. P) and one equivalence test (T vs. R) all pass. An accurate estimate of the nuisance parameter (e.g. variance) is vital in determining an accurate sample size to attain sufficient power. However, due to potential study design variations between NDA and Abbreviated NDA (ANDA) studies and high variability of clinical endpoints, variance may be over- or under-estimated, resulting in unnecessary extra costs or underpowered studies. Traditionally, clinical endpoint BE studies use a fixed study design. In this work, we propose four sample size re-estimation approaches based on a nuisance parameter and recommend one approach after comparing various operating characteristics by simulation.

The proposed adaptive design with sample size re-estimation provides a more accurate estimate of sample size without wasting resources or under-powering the study and controls the Type 1 error rate under a negligible level, both for the family-wise alpha and individual alpha for superiority and equivalence tests.

临床终点生物等效性（BE）研究通常用于在局部作用的通用药物（T）与创新药物（R）之间建立生物等效性（BE），创新药物是双盲，随机三臂疗法（T，R和安慰剂：P）平行临床试验。如果两项合格测试（T与P，R与P）和一项等效测试（T与R）全部通过，则建立BE。干扰参数（例如方差）的准确估算对于确定准确的样本量以获得足够的功效至关重要。但是，由于NDA和NDA缩写研究之间可能存在的研究设计差异，以及临床终点的高度可变性，差异可能会被高估或低估，从而导致不必要的额外费用或研究能力不足。传统上，临床终点BE研究使用固定的研究设计。在这项工作中

提出的具有样本量重新估计的自适应设计可提供更准确的样本量估计，而不会浪费资源或不足以支持研究，并且将1型错误率控制在可忽略的水平内，无论是针对家庭的α还是针对个体的α优越性和等效性测试。