## 3 Results  
The first exploration in this study, referred to the condition when both the T and R pharmaceutical products have equal means and a low coefficient of variation at 15%. This scenario was investigated for various sample and subsample sizes (Figure 2). As illustrated in Figure 2, the BE acceptance rate increases with the rise in the actual sample size. In addition, the percentage of acceptance for the \"subsampled\" group is always lower than the \"original\", whereas the acceptance rate of the \"VAE-generated\" group of subjects is always higher than the original and, thus, the \"subsampled\" also. Also, as expected, for all the three datasets (original, subsampled, and VAE-generated), the acceptance rate tends to decrease when the subsample proportions decline or when the sample size gets lower. Though, for the original and the subsampled datasets, this decrease is more extreme than the generated. More specifically, for the \"generated\" dataset, the acceptance rate starts from 60% when the sample size and the subsample proportion are small and increases up to 100% while the sample size and subsample proportion increase. This trend is similar for the original and subsampled datasets, though the acceptance rate for the original dataset reaches its lowest (around 40%) for sample size 12, while, for the subsampled dataset, the lowest is reached even at the sample size of 24 for low subsample proportions (around 10%). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Between-Subject** & **Within-Subject** & **Mean Endpoint** & **Ratio of Average** & **Original** & **Subsampled** & **Size of** **Generated** \\
**Variability (CV)** & **Variability** & **Value for the** & **Endpoints** & **Test/Reference** & **(N)** & **Data (xN)** \\ \hline
20\% & 15\% & 100 & 1 & 12 & 25\% & 1\(\times\) \\ \hline  & 30\% & & 1.1 & 24 & 50\% & 2\(\times\) \\ \hline  & & & 1.2 & 48 & 75\% & \\ \hline  & & & & & 72 & \\ \hline \end{tabular}
\end{table} 
Table 2: List of factors explored in this study. For all cases, the mean of the stochastic term, mentioned in step “II” above, was set equal to 1. Figure 3 illustrates the probability of acceptance for the same sample sizes and proportion rates between the R and T groups for low CVw (15%) when the mean of the T group is 10% and 20% higher than the one of the R group (i.e., for T/R ratios of 1.1 and 1.2). Figure 3 illustrates the trend of BE acceptance as both sample size and subsample proportions increase from left to right. This analysis is conducted with the T/R ratio of  
Figure 3: Bioequivalence acceptance rate (%) for the “original”, “subsampled”, and VAE-“generated” datasets when there is 10% (**A**) and 20% (**B**) difference in the average endpoint value between the Test (T) and Reference (R) pharmaceutical products. Four different original sample sizes (N) were utilized (12, 24, 48, and 72). The subsample proportions were 25%, 50%, 75%, and 100%. Figure 2: Bioequivalence acceptance rate (%) for the “original”, “subsampled”, and VAE-“generated” datasets when both the Reference (R) and Test (T) pharmaceutical products exhibit identical average performance. Four different “original” sample sizes (N) were utilized (12, 24, 48, and 72). The subsample proportions were 25%, 50%, 75%, and 100%. the average endpoint set at 1.1 in the top row and 1.2 in the bottom row. Notably, the acceptance rate of the generated data is significantly higher than that of the original and subsample data. This difference is particularly evident when both the sample size and proportion rate are lower, as shown in Figure 3A,B. As expected, a larger difference between the means of R and T groups corresponds to a lower acceptance rate across all groups. It is essential to emphasize that the acceptance rate of the generated data consistently exceeds that of the other two datasets. Figure 4 similarly illustrates the acceptance rates for a 30% within-subject variability of the measured BE endpoint and several values of the T/R ratio (1.0, 1.1, and 1.2).