Impact of Logarithmic Transformation on the Restoration of Normality in Bioequivalence Data


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

How to Cite

Ghazala Ishrat, Munther Al-Shami, Nawaz, M., & Rizwani, G. H. (2017). Impact of Logarithmic Transformation on the Restoration of Normality in Bioequivalence Data. Journal of Basic & Applied Sciences, 13, 597–605.


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.


Agency EM. Guideline on the investigation of bioequivalence 2010. (CPMP/EWP/QWP/1401/98 Rev. 1/ Corr**). London: European Medicines Agency Retrieved from

Multisource (generic) pharmaceutical products; guidelines on registration requirements to establish interchangeability. Annex 7, WHO Technical Report Series, 937, World Health Organization, 2006. documents/ TRS937/WHO_TRS_937_eng.pdf#page=359. Accessed 5 Apr 2013. 36. Mexico

FDA. Guide for Indusry; Statistical Approaches to Establishing Bioequivalence. USA: U.S. Department of health and Human Services 2001.

Canada H. Conduct and Analysis of Comparative Bioavailability Studie. (May 22, 2012). Canada: Minister of Public Works and Government Services Retrieved from Minister of Public Works and Government Services 2012.

Dr. Adnan Badwan. Jordanian Pharmaceutical Manufacturing (JPM) Co. PLC, Na’our – Jordan. Conducted at ACDIMA center for Bioequivalence and Pharmaceutical Studies at Ibn Al-Haitham Hospital, Amman. (e-mail:

Wilcoxon F. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1945; 1(6): 80-83. doi: citeulike-article-id:1712538, doi: 10.2307/3001968

Hauschke D, Steinijans VW, Diletti E. A distribution-free procedure for the statistical analyses of bioequivalence studies. International Journal of Clinical Pharmacology, Therapy and Toxicology 1990; 28: 72-78.

Hodges JL, Lehmann EL. Estimates of location based on rank tests. The Annals of Mathematical Statistics 1963; 34: 598-611.

Kowalski J, Tu XM. Modern Applied U Statistics. New York: Wiley 2007

Tang W, He H, Tu XM. Applied categorical and count data analysis. FL: Chapman & Hall/CRC 2012.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2017 Ghazala Ishrat , Munther Al-Shami, Muhammad Nawaz, Ghazala H. Rizwani