Molins Eduard, Cobo Erik, Ocaña Jordi
Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Spain.
Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, Spain.
Stat Med. 2017 Dec 30;36(30):4777-4788. doi: 10.1002/sim.7452. Epub 2017 Aug 29.
The usual approach to determine bioequivalence for highly variable drugs is scaled average bioequivalence, which is based on expanding the limits as a function of the within-subject variability in the reference formulation. This requires separately estimating this variability and thus using replicated or semireplicated crossover designs. On the other hand, regulations also allow using common 2 × 2 crossover designs based on two-stage adaptive approaches with sample size reestimation at an interim analysis. The choice between scaled or two-stage designs is crucial and must be fully described in the protocol. Using Monte Carlo simulations, we show that both methodologies achieve comparable statistical power, though the scaled method usually requires less sample size, but at the expense of each subject being exposed more times to the treatments. With an adequate initial sample size (not too low, eg, 24 subjects), two-stage methods are a flexible and efficient option to consider: They have enough power (eg, 80%) at the first stage for non-highly variable drugs, and, if otherwise, they provide the opportunity to step up to a second stage that includes additional subjects.
确定高变异药物生物等效性的常用方法是标化平均生物等效性，它基于根据参比制剂的个体内变异扩大限度。这需要分别估算这种变异，因此要采用重复或半重复交叉设计。另一方面，法规也允许基于两阶段适应性方法使用常见的2×2交叉设计，并在期中分析时重新估算样本量。标化设计或两阶段设计之间的选择至关重要，必须在方案中充分说明。通过蒙特卡洛模拟，我们表明两种方法都能达到相当的统计效能，尽管标化方法通常需要的样本量较少，但代价是每个受试者接受治疗的次数更多。有了足够的初始样本量（不能太低，例如24名受试者），两阶段方法是一个值得考虑的灵活且有效的选择：对于非高变异药物，它们在第一阶段有足够的效能（例如80%），否则，它们提供了进入包括额外受试者的第二阶段的机会。
