Mayer Benjamin, Stahl Vicky, Kron Martina
Institute of Epidemiology and Medical Biometry, Ulm University, Germany.
Altern Lab Anim. 2017 Dec;45(6):317-328. doi: 10.1177/026119291704500608.
Statistical sample size calculation is essential when planning animal experiments in basic medical research. Usually, such trials involve the testing of multiple hypotheses, and interpreting them in a confirmative manner would require the appropriate adjustment of the Type 1 error. This has to be taken into account as early as possible during sample size estimation - otherwise, all the results obtained would be exploratory, i.e. without cogency. In this paper, the concept of gatekeeping is introduced, along with alternative approaches for Type 1 error adjustment. The application of gatekeeping to the calculation of sample size is demonstrated by using data sets from case studies. Overall, the evaluation of these examples showed that gatekeeping is able to keep the required number of animals comparatively small. In contrast to exploratory planning, which led to the lowest sample sizes, gatekeeping suggested a mean increase of 12% in sample size, while conservative Bonferroni adjustment raised the sample size by 34% on average. Gatekeeping is a prominent strategy for handling the multiple testing problem, and has been proven to keep the required sample sizes in animal studies comparatively low. Therefore, it is a suitable approach to a compromise between the Three Rs principle of reduction and the appropriate handling of the multiplicity issue in animal trials with a confirmative focus.
在基础医学研究中规划动物实验时，统计样本量计算至关重要。通常，此类试验涉及多个假设的检验，而以验证性方式解释这些假设需要对I型错误进行适当调整。在样本量估计过程中必须尽早考虑到这一点——否则，所有获得的结果都将是探索性的，即缺乏说服力。本文介绍了把关的概念以及I型错误调整的替代方法。通过使用案例研究的数据集展示了把关在样本量计算中的应用。总体而言，对这些示例的评估表明，把关能够使所需动物数量相对较少。与导致样本量最小的探索性规划相比，把关建议样本量平均增加12%，而保守的Bonferroni调整平均使样本量增加34%。把关是处理多重检验问题的一个突出策略，并且已被证明能使动物研究中所需的样本量相对较低。因此，它是在减少原则的“3R”原则与以验证为重点的动物试验中适当处理多重性问题之间达成妥协的合适方法。
