Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz.
Dtsch Arztebl Int. 2012 Oct;109(41):674-9. doi: 10.3238/arztebl.2012.0674. Epub 2012 Oct 12.
An increasing number of clinical trials are being performed to show the absence of relevant differences between the effects of two treatments. The primary care physician makes use of the results of so-called equivalence studies, at least indirectly, practically every day. Equally important are active control clinical trials in which the efficacy of a new treatment has to be proven through demonstrating non-inferiority as compared to a standard treatment.
Explanation of basic principles and statistical techniques with reference to the original literature; selective searches in the medical literature.
First of all, a suitable distributional parameter must be chosen that can be considered a reasonable measure of dissimilarity of the population effects of the treatments under comparison. The simplest approach to the statistical demonstration of equivalence or non-inferiority is to calculate confidence intervals for that parameter. To keep the required number of subjects for equivalence and non-inferiority studies as low as possible, statistical tests should be used which are optimized with respect to power.
Data from equivalence and non-inferiority studies need to be assessed for statistical significance no less than data that are generated to show that two treatments have different effects. A negative result in a traditional two-sided test does not suffice for statistically proving equivalence.
越来越多的临床试验旨在证明两种治疗方法的效果没有显著差异。基层医生至少间接地每天都在使用所谓的等效性研究的结果。同样重要的是积极的对照临床试验，在这些试验中，新治疗方法的疗效必须通过与标准治疗相比证明非劣效性来证明。
参考原始文献解释基本原理和统计技术；选择性搜索医学文献。
首先，必须选择合适的分布参数，该参数可以被认为是比较治疗效果的群体差异的合理度量。统计证明等效性或非劣效性的最简单方法是计算该参数的置信区间。为了尽可能降低等效性和非劣效性研究所需的样本量，应使用针对功效进行优化的统计检验。
等效性和非劣效性研究的数据需要进行统计学意义评估，不亚于为证明两种治疗方法有不同效果而生成的数据。传统的双侧检验的阴性结果不足以从统计学上证明等效性。
