## 3 Results  
The first step of this study was to explore the BE acceptance rates for all three types of datasets (i.e., original, subsampled, and generated) under the condition that both the T and R drugs have equal means in the measured endpoint. Figure 3 illustrates the BE acceptance rates for different coefficients of variation of 20% (Figure 3A), 40% (Figure 3B), and 60% (Figure 3C). For the highly variable cases (40% and 60%), the scaled BE limits, imposed by the EMA or FDA, were used in the case of the original and subsampled datasets in accordance with the regulatory guidelines [3]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Original Sample** & \begin{tabular}{c} **Within-Subject** \\ **Variability** \\ **(CV\({}_{\text{W}}\))** \\ \end{tabular} & \begin{tabular}{c} **Ratio of Average** \\ **Endpoints** \\ **Test/Reference** \\ \end{tabular} & \
\begin{tabular}{c} **Between-** \\ **Subject** \\ **Variability (CV)** \\ \end{tabular} &
\begin{tabular}{c} **Size of** \\ **Generated** \\ **Dataset (xN)** \\ \end{tabular} \\ \hline
12 & 20\% & 1 & 20\% & 1\(\times\) \\ \hline
24 & 40\% & 1:1 & & 2\(\times\) \\ \hline
48 & 60\% & & & 3\(\times\) \\ \hline
72 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Factors relevant to the simulated bioequivalence studies explored in this study. For all cases, the mean of the stochastic term expressing within-subject variability was set to 1. No scaling was used for the VAE-generated datasets, in order to be treated more strictly. Aiming to assess the statistical power achieved, the BE acceptance rates were explored for various sample and subsample sizes. Figure 3 shows that for all datasets, the acceptance rate decreases as the coefficient of variation increases, while the power increases for larger original and subsample sizes. It is shown that the percentage of acceptance of the subsampled set is always lower than that of the original, whereas the acceptance rate for the generated is always higher than that of the original dataset. It is important to emphasize that for 40% and 60% variabilities, the acceptance limits when conducting BE testing for the generated datasets, did not follow the scaled BE approach, while the original and the subsampled datasets were extended as a function of CVW, in line with the EMA guidelines. Figure 3: Acceptance rates of the original, subsampled, and generated datasets. In all cases, the average test/reference ratio is 1 and the within-subject variability equals 20% (**A**), 40% (**B**), and 60% (**C**). Three different original sample sizes are used (24, 48, and 72), while the sizes of the generated datasets are 1, 2, and 3 times that of the original sample. The bioequivalence assessment for the original and subsampled dataset is performed using the EMA and FDA scaled bioequivalence approaches [1,2], while no scaling is applied to the VAE-generated datasets. The subsample proportions used are 50%, 75%, and 100%. A similar behavior is observed for the same scenarios under the assumption that the ratio of the means of the T and R pharmaceutical products is 1:1, which is displayed in Figure 4. There is an increasing trend of the acceptance rate for all types of datasets when the sample and subsample size increases for both the FDA and the EMA. The original datasets outperform the subsampled datasets in all cases, whereas the VAE-generated datasets outperform the original datasets for CV = 20% (Figure 4A) and CV = 40% (Figure 4B). It is important to underline that for the scenarios presented in Figure 4, the acceptance limits for the VAE-generated datasets were not extended, in contrast with the acceptance limits for the original and subsampled cases. To obtain a clearer comparison between the performance of the VAE approach and the classic/scaled limits, the percentage of \"acceptance gains\" of BE were calculated for each  
Figure 4: Acceptance rates of the original, subsampled, and generated datasets.