This is the data of the first period from Clayton and Leslie ( 6) with balance between treatment groups, i.e., equal sizes of the test treatment ( N T) and the reference treatment ( N R): N T = N R = 9.īased on dataset P1, where subjects 10…14 were removed ( N T = 9, N R = 4).īased on dataset P1, where the raw data entry for subject 4 has been multiplied by 100. The characteristics of datasets are as follows: The datasets used in this paper seek to include small and large data sets, outliers, unequal group sizes, and heteroscedasticity as a type of stress test.
It is not the aim of this work to validate any software or to advocate for or against any specific software package. It is outside the scope of this paper to discuss more than two groups or the other design options that exist such as replicate designs.įor validation purposes, datasets should be of varying complexity in terms of imbalance, outliers, range, heteroscedasticity, and point estimate in order to cover any situation which can reasonably be expected to occur in practice. Since trials with two parallel groups are the second most common type of bioequivalence studies and published datasets with known results are scarce, the purpose of this paper is to propose reference datasets for two-group parallel trials and derive 90% confidence intervals with different statistical software packages in order to establish consensus results that can be used-together with the datasets-to qualify or validate software analyzing the outcomes from parallel group bioequivalence trials. On that basis, we recently published a paper in this journal with reference datasets for the two-treatment, two-sequence, two-period crossover trials and where the datasets were evaluated with different software packages ( 5). To evaluate the data obtained in a bioequivalence trial, companies must use validated software but in the absence of datasets with known results, it is difficult to actually know if the software acquired correctly performs the task it is supposed to do and therefore, it is practically impossible to validate software in-house beyond installation qualification and operational qualification. The former is considerably more common than the latter and the design of choice for active ingredients whose half-lives are not prohibitively long ( 1– 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 α 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 (“test”) with that of the known formulation (“reference”). Bioequivalence 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.