Comparison of Parametric and Bootstrap Method in Bioequivalence Test.
10.4196/kjpp.2009.13.5.367
- Author:
Byung Jin AHN
1
;
Dong Seok YIM
Author Information
1. Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea. yimds@catholic.ac.kr
- Publication Type:Original Article
- Keywords:
Bioequivalence;
Bootstrap;
Confidence interval;
Nonparametric method
- MeSH:
Area Under Curve;
Confidence Intervals;
Phenothiazines;
Therapeutic Equivalency
- From:The Korean Journal of Physiology and Pharmacology
2009;13(5):367-371
- CountryRepublic of Korea
- Language:English
-
Abstract:
The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled datasets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.
