Impact of Logarithmic Transformation on the Restoration of Normality in Bioequivalence Data
Pages 597-605

Ghazala Ishrat and Munther Al-Shami

DOI: https://doi.org/10.6000/1927-5129.2017.13.96

Published: 16 November 2017

Abstract: The Logarithmic Transformation is widely used to address the skewness and assumes the normality assumption of the bioequivalence data but this may not be true in all cases unless the underlying assumption is taken into account and verified that the randomly generated data is normally distributed in the BE studies. Instead of restoring the normality in the data, the Log-Transformation may introduce new problems like inducing skewness with an increase in variability, which are even more difficult to deal with, then the original problem of non-normal distribution of data. Pharmacokinetic parameters, derived from the real biodata of the bioequivalence study of Glimepiride 4mg tablet was statistically analyzed, with and without, Log-Transformation through ANOVA and the two were compared for normality assumption through the standard testing for normality like Shapiro-Wilk and Q-Q Plots. The comparison of the conclusive results from both approaches, linear and log-transformed data, does not conclude any significant difference. A further investigation is required to strengthen this notion and to identify the circumstances and situations where the deterministic parameters are ascertained to select a suitable model for the data analysis and conclusion. The alternative analytic methods that eliminate the need of transforming non-normal data distributions prior to analysis, like Wilcoxon-Mann-Whitney two one-sided test which has been recommended by Hauschke et al., Hodges-Lehmann estimator or the other newer analytic distribution-free methods, that are not dependent on the distribution of data like the generalized estimating equations (GEE) are recommended.

Keywords: Bioequivalence, Log-transformation, Normality, Normal Distribution, Log-Normal Distribution, Skewness, Confidence Interval, Hypothesis testing, Outliers

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