The use of asymmetric distributions in average bioequivalence

• Published in: Statistics in medicine 2016-07, Vol.35 (15), p.2525-2542
• Main Authors: de Souza, Roberto Molina, Achcar, Jorge Alberto, Martinez, Edson Zangiacomi, Mazucheli, Josmar
• Format: Article
• Language: English
• Subjects: Asymmetry; average bioequivalence; Bayes Theorem; Bayesian inference; Commercialization; copula functions; Data Interpretation, Statistical; Drugs, Generic; extended generalized gamma distribution; Generic drugs; Humans; multivariate skew-t distribution; Probability distribution; Statistical analysis; Statistical Distributions; Therapeutic Equivalency
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.6885

Generic drugs have been commercialized in numerous countries. Most of these countries approve the commercialization of a generic drug when there is evidence of bioequivalence between the generic drug and the reference drug. Generally, the pharmaceutical industry is responsible for the bioequivalence test under the supervision of a regulatory agency. This procedure is concluded after a statistical data analysis. Several agencies adopt a standard statistical analysis based on procedures that were previously established. In practice, we face situations in which this standard model does not fit to some sets of bioequivalence data. In this study, we propose an evaluation of bioequivalence using univariate and bivariate models based on an extended generalized gamma distribution and a skew‐t distribution, under a Bayesian perspective. We introduce a study of the empirical power of hypothesis tests for univariate models, showing advantages in the use of an extended generalized gamma distribution. Three sets of bioequivalence data were analyzed under these new procedures and compared with the standard model proposed by the majority of regulatory agencies. In order to verify that the asymmetrical distributions are usually better fitted for the data, when compared with the standard model, model discrimination methods were used, such as the Deviance Information Criterion (DIC) and quantile–quantile plots. The research concluded that, in general, the use of the extended generalized gamma distribution may be more appropriate to model bioequivalence data in the original scale. Copyright © 2016 John Wiley & Sons, Ltd.