## 3 Results
Regarding the \"sample\", with the increase in the T/R ratio, the successes were reduced. As expected, when the CV increases the successes of the \"sample\" increased. The number of successes for the \"sample\" were higher than the \"original\" and the \"generated\" population because of the small number of values. This also  
Figure 4: Distribution of the measured endpoints for the “original” (population), “sample” (50 subjects), and WGAN-“generated” datasets. The coefficient of variation for between-subject variability is 20%, while the Test/Reference ratio refers to 1.00 (**A**) and 1.05 (**B**). supports the fact that the \"sample\" cannot reflect the \"original\" population, except when the variability is as low as 10% and the T/R ratio is equal to 1.0. Figure 5: Percent similarity in the performance among the “original”, “sample”, and “generated” datasets when the size of the “sample” dataset is fixed at 50 subjects. The average Test/Reference (T/R) ratio is equal to 1.00 (**A**), 1.05 (**B**), 1.10 (**C**), and 1.15 (**D**). Four levels (10%, 20%, 50%, 70%) of between-subject variability were explored. Under each scenario, a number of 5000 Monte Carlo clinical trials are simulated. Figure 6: Percent of success for the “original”, “sample”, and “generated” datasets when the size of the “sample” dataset is fixed at 50 subjects. In all cases, the Test/Reference ratio is equal to unity. Four between-subject variability levels are used (10%, 20%, 50%, 70%). A success in the \(t\)-test evaluation means that the two treatments under comparison (Test vs. Reference) do not differ significantly (at the 5% level). Under each scenario, a number of 5000 Monte Carlo clinical trials are simulated. After analyzing the results of the 0.5% sampling, a 1.0% sampling was conducted, representing 100 patients from the initial population of 10,000. Figure 7 provides a visual summary of the similarity percentages observed in this scenario. From the 16 scenarios explored, the similarity percentages between the \"original\" and the \"generated\" population were greater than those between the \"original\" and the \"sample\" in 9 scenarios. For the remaining 7 scenarios, the similarities were equal. Notably, an increase in the \(\mathrm{T/R}\) ratio corresponded to a decrease in similarity as the between-subject variability (in terms of CV) increased. Generally, WGANs exhibited a better handling of high variability compared with the sample, except when \(\mathrm{T/R}\) equaled to 1.0, where the WGANs and the sample exhibited almost the same results. As in the case of Figure 4, where the \"sample\" referred to 50 subjects, there is a clear similarity in the shape of distributions between the \"original\" and the \"generated\" datasets when the \"sample\" equals 100 subjects (Figure A1). Again, it is evident that the \"generated\" distributions closely resemble the \"original\". It should be highlighted that the \"sample\" distributions failed to capture the \"original\" shape, whereas WGANs successfully generated a population that mirrored the original. The success percentages for the 1.0% sampling (sample size = 100) were then visualized, in the case of \(\mathrm{T/R}\) = 1 (Figure 8). It can be observed that the \"generated\" population demonstrated a higher success percentage than the \"original\", while the \"sample\" exhibited a higher success percentage than both. Across all scenarios, the \"generated\" population showed either a higher or equal success percentage compared with the \"original\" population, while the \"sample\" had a higher success percentage in 13 out of 16 scenarios, with an equal success percentage observed in the remaining 3 scenarios compared with the \"original\" and \"generated\" population simultaneously. Even with a larger sample  
Figure 7: Percent similarity in the performance among the “original”, “sample”, and “generated” datasets when the size of the “sample” dataset is 100. The average Test/Reference (\(\mathrm{T/R}\)) ratio is equal to 1.00 (**A**), 1.05 (**B**), 1.10 (**C**), and 1.15 (**D**).