Lauzon Carolyn, Caffo Brian
Department of Biophysics and Department of Biostatistics, Johns Hopkins University.
Am Stat. 2009 May 1;63(2):147-154. doi: 10.1198/tast.2009.0029.
Equivalence testing is growing in use in scientific research outside of its traditional role in the drug approval process. Largely due to its ease of use and recommendation from the United States Food and Drug Administration guidance, the most common statistical method for testing equivalence is the two one-sided tests procedure (TOST). Like classical point-null hypothesis testing, TOST is subject to multiplicity concerns as more comparisons are made. In this manuscript, a condition that bounds the family-wise error rate using TOST is given. This condition then leads to a simple solution for controlling the family-wise error rate. Specifically, we demonstrate that if all pair-wise comparisons of k independent groups are being evaluated for equivalence, then simply scaling the nominal Type I error rate down by (k - 1) is sufficient to maintain the family-wise error rate at the desired value or less. The resulting rule is much less conservative than the equally simple Bonferroni correction. An example of equivalence testing in a non drug-development setting is given.
等效性检验在药物审批过程之外的科学研究中的应用正在不断增加。主要由于其使用简便以及美国食品药品监督管理局指南的推荐，用于检验等效性的最常见统计方法是双单侧检验程序（TOST）。与经典的点零假设检验一样，随着进行的比较增多，TOST也存在多重性问题。在本手稿中，给出了一个使用TOST限制族错误率的条件。该条件进而引出了一个控制族错误率的简单解决方案。具体而言，我们证明，如果对k个独立组的所有成对比较进行等效性评估，那么简单地将名义I型错误率按(k - 1)进行缩放就足以将族错误率维持在期望的值或更低。由此产生的规则比同样简单的邦费罗尼校正要不那么保守。给出了一个非药物研发环境中等效性检验的示例。
