Analysis of equivalence test properties for log-normal data

Recieved: 06/24/2025

Accepted: 07/12/2025

Keywords: hypothesis, lognormal distribution, significance level, bioequivalence

To cite this article

Dranitsyna M.A., Zakharova T.V., Klimenko V.K. Analysis of equivalence test properties for log-normal data. // Moscow University Journal. Series 15. Computational Mathematics and Cybernetics. 2025. N 4, p.26-37 https://doi.org/10.55959/MSU/0137–0782–15–2025–49–4–26–37.

This work is licensed under a Сreative Commons Atribiution - NonCommercial 4.0 International (CC BY-NC 4.0)

Dranitsyna M.A. (Scientific Consulting Center LLC)
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Zakharova T.V. (Lomonosov Moscow State University (MSU))
Klimenko V.K. (Lomonosov Moscow State University (MSU))

Abstract

The aim of equivalence testing is to verify that two parameters are sufficiently close or, alternatively, that the parameter of interest lies within two pre-specified limits. The two one-sided tests procedure is arguably the most widely known approach for assessing equivalence in the pharmaceutical field. Using a model that accounts for missing data, it is shown analytically that the type I error rate may exceed the nominal significance level. A refined estimate of this error is also obtained. For the standard 2x2 crossover design, a method is proposed that enables control of the type I error in the presence of missing data.