Equivalence trials aim to demonstrate that two treatments do not differ by more than a prespecified clinically irrelevant amount. We consider the problem when equivalence is defined in terms of the ratio of population means and the original (untransformed) data are normally distributed. Application
Testing Equivalence Simultaneously for Location and Dispersion of two Normally Distributed Populations
✍ Scribed by Peter Bauer; Michael M. Bauer
- Publisher
- John Wiley and Sons
- Year
- 1994
- Tongue
- English
- Weight
- 981 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0323-3847
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✦ Synopsis
Abstract
In clinical trials with an active control usually therapeutical equivalence of a new treatment is investigated by looking at a location parameter of the distributions of the primary efficacy variable. But even if the location parameters are close to each other existing differences in variability may be connected with different risks for under or over treatment in an individual patient.
Assuming normally distributed responses a multiple test procedure applying two shifted one‐sided t‐tests for the mean and accordingly two one‐sided F‐tests for the variances is proposed. Equivalence in location and variability is established if all four tests lead to a rejection at the (one‐sided) level α. A conservative procedure “correcting” the t‐tests for heteroscedasticity is derived. The choice of a design in terms of the global level α, the global power, the relevant deviations in the population means and variances, as well as the sample size is outlined. Numerical calculations of the actual level and power for the proposed designs show, that for balanced sample sizes the classical uncorrected one‐sided t‐tests can be used safely without exaggerating the global type I error probability.
Finally an example is given.
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