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Abstract <p>The purpose of equivalence testing is to verify that two parameters are sufficiently close or, alternatively, that the parameter under consideration lies between two predetermined limits. The procedure of two one-sided tests is perhaps the best-known approach to assessing equivalence in the pharmaceutical field. Using a model that accounts for missing data, it is shown analytically that a Type I error can exceed the specified significance level. A refined estimate of this error is also obtained. A way of controlling Type I errors when there are missing data is proposed for a <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(2\times 2\)</EquationSource> <!--CMatCMGU2570020Dranitsyna-m1--> </InlineEquation> crossover design.</p>

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Analyzing the Properties of an Equivalence Test for a Lognormal Data Distribution

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • M. A. Dranitsyna,
  • T. V. Zakharova,
  • V. K. Klimenko

摘要

Abstract

The purpose of equivalence testing is to verify that two parameters are sufficiently close or, alternatively, that the parameter under consideration lies between two predetermined limits. The procedure of two one-sided tests is perhaps the best-known approach to assessing equivalence in the pharmaceutical field. Using a model that accounts for missing data, it is shown analytically that a Type I error can exceed the specified significance level. A refined estimate of this error is also obtained. A way of controlling Type I errors when there are missing data is proposed for a \(2\times 2\) crossover design.