Comparison of Parametric and Bootstrap Method in Bioequivalence Test

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.

Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease

“…Another example with Λ R , implementation C and CTRL/MCI-co (AUC=0.78) shows a substantially similar interval width of all methods (0.74 − 0.82 Hanley, Mann-Whitney; 0.73 − 0.84 Maximum Variance). The bias-corrected percentile bootstrap was regarded as a safe estimate as it did not require any assumption about the normality of the log-transformed AUC(Ahn and Yim, 2009).…”

Development of a bootstrap-model for determining the release of medicinal preparations in the human organism

“…The bootstrap method makes it possible to easily and rapidly evaluate statistical characteristics (confidence intervals, dispersion, correlation and so on) for complex models, without the need to rely on the a priori assumptions about the nature of distribution [16]. In recent years, this method has been applied in the medical statistics, for example, when establishing the bioequivalence of medicines based on the clinical and preclinical studies [17][18][19].…”