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Review
. 2012 Oct;109(41):674-9.
doi: 10.3238/arztebl.2012.0674. Epub 2012 Oct 12.

Establishing Equivalence or Non-Inferiority in Clinical Trials: Part 20 of a Series on Evaluation of Scientific Publications

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Free PMC article
Review

Establishing Equivalence or Non-Inferiority in Clinical Trials: Part 20 of a Series on Evaluation of Scientific Publications

Stefan Wellek et al. Dtsch Arztebl Int. .
Free PMC article

Abstract

Background: 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.

Methods: Explanation of basic principles and statistical techniques with reference to the original literature; selective searches in the medical literature.

Results: 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.

Conclusion: 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.

Figures

Figure 1
Figure 1
Frequency of equivalence trials: results of a literature search
Figure 2
Figure 2
Visualization of the procedure described in Box 1a: values above (below) the numerical axis relate to the treatment difference in the population (in the samples); * = observed mean difference

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