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The former is considerably more common than the latter and the design of choice for active ingredients whose half-lives are not prohibitively long (1вЂ“4). The most common designs for bioequivalence testing are the two-treatment, two-sequence, two-period randomized crossover design and the randomized two-group parallel design. A confidence interval is then constructed on basis of two one-sided t tests, typically at a nominal О± level of 5%. Using non-compartmental analysis, the primary metrics derived in bioequivalence studies are most often the area under the concentration time curve until the last sampling point ( AUC t) and the maximum observed concentration ( C max) for both the test and the reference. Throughout many countries and jurisdictions, the common way of testing for bioequivalence is to compare the pharmacokinetics of the new formulation (вЂњtestвЂќ) with that of the known formulation (вЂњreferenceвЂќ). KEY WORDS: bioequivalence, parallel design, software validation INTRODUCTIONД«ioequivalence testing is a general requirement for companies developing generic medicines, testing food effects, making formulation changes, or developing extensions to existing approved medicines where absorption rate and extent to the systemic circulation determines safety and efficacy. The online version of this article (doi:10.1208/s1224-6) contains supplementary material, which is available to authorized users. A proposal is made for the results that should be used as validation targets. Where results could be obtained across all packages, one showed questionable results when datasets contained unequal group sizes ( Kinetica). Not all software packages provide functionality for the assumption of unequal variances (EquivTest, Kinetica), and not all packages can handle datasets with more than 1000 subjects per group ( WinNonlin). Insofar as possible, datasets were evaluated with and without the assumption of equal variances for the construction of a 90% confidence interval. This paper puts a total 11 datasets into the public domain along with proposed consensus obtained via evaluations from six different software packages ( R, SAS, WinNonlin, OpenOffice Calc, Kinetica, EquivTest). In order to help companies qualify and validate the software used to evaluate bioequivalence trials with two parallel treatment groups, this work aims to define datasets with known results.

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