## 3 Results
In all cases, the average test/reference ratio is 1:1 (i.e., 10% mean difference) 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 datasets 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%. scenario studied. These acceptance gains were defined as the acceptance rate of the VAE-generated dataset minus the acceptance rate of the original dataset. The acceptance gains were calculated for all the scenarios listed in Table 2 and are presented in Figures 5 and 6. In Figure 5, the ratio of the means of the T and R pharmaceutical products is equal to 1, while in Figure 6 the T/R ratio is 1:1 The scenarios that are not shown in Figures 5 and 6 refer to the cases where there was no acceptance gain, namely, both the original and the generated datasets exhibited similar performance in terms of the statistical power. Figure 5 illustrates that the acceptance gain for the EMA scenarios (Figures 5A and 5B for 24 and 48 subjects, respectively) is achieved when the CV and sample size are equal to 60% and 24, respectively. For the FDA case (Figure 5C), the acceptance gain ranges from 10% to 15% for the scenario where the CV equals 20% and the sample size equals 24. For both regulatory authorities, the acceptance gain increases overall as the subsample size increases. Figure 6 presents the acceptance gain for the scenario where T/R equals 1:1. Figure 6A, 6B and 6C show the acceptance gain when the limits are extended according to the EMA for sample sizes of 24, 48, and 72, respectively. Figure 6D-F show the acceptance gain when the limits are extended according to the FDA for the pre-mentioned sample sizes. In the case of the EMA (Figure 6A-C), the acceptance gain ranges from 5% when the CV and sample size refer to 40% and 48%, respectively, to 48% for the scenario where the CV equals 20% and the sample size equals 24%. The acceptance gain range in the FDA cases (Figure 6D-F) is similar to that of the EMA, though there are more scenarios where no acceptance gain was observed. Figure 5: Acceptance gains, namely, the difference in the acceptance rate percentages between the VAE-generated and the original datasets. The acceptance limits for the original datasets are increased according to the EMA (**A**,**B**) and FDA (**C**) guidelines. In all cases, the average test/reference ratio is 1 and the within-subject variability equals 20%, 40%, and 60%. Three different original sample sizes are used (24, 48, and 72), while the size of the generated datasets is equal (1\(\times\)) to that of the original sample. The scenarios where the generated and original datasets perform identically are not shown due to space restrictions. Figures 7 and 8 show the percentage of acceptance gain when no scaling is applied to the original dataset. Therefore, this refers to a condition where both the VAE-generated and the original datasets are treated equally. Scenarios for T/R equal to 1 and 1:1 are shown, along with sample sizes of 24, 48, and 72. Figure 6: Acceptance gains, namely, the difference in the acceptance rate percentages between the VAE-generated and the original datasets. The acceptance limits for the original datasets are increased according to the EMA (**A–C**) and FDA (**D–F**) guidelines. In all cases, the average test/reference ratio is 1:1 and the within-subject variability equals 20%, 40%, and 60%. Three different original sample sizes are used (24, 48, and 72), while the size of the generated datasets is equal (1\(\times\)) to that of the original sample. The scenarios where the generated and original datasets perform identically are not shown due to space restrictions.