## 3 Results
Four levels (10%, 20%, 50%, 70%) of between-subject variability are explored. Under each scenario, a number of 5000 Monte Carlo clinical trials are simulated. percentage (twice the size of the one shown before), the \"sample\" exhibited an inferior behavior compared with the \"original\" or \"generated\" datasets in terms of similarity and statistical findings. In all previous scenarios, the size of the \"generated\" population was set to be equal to that of the \"original\" population, which was 10,000 subjects, and the observed performance of the WGAN procedure was found to be favorable. To further investigate the WGAN performance, smaller sizes were also explored for the \"generated\" datasets. In this context, Figure 9 presents the percent similarity in the performances of the three datasets when smaller sizes were used for the \"generated\" datasets, specifically referring to 1000, 500, and 100 subjects. In all these cases, the between-subject variability was set to 10%, the T/R ratio took four values (1.0, 1.05, 1.10, 1.15), and the size of the intermediate \"sample\" was either 50 or 100. Across all three \"generated\" sizes (i.e., \(1000,500,100\)) and two \"sample\" sizes (50 and 100), the similarity percentages were similar, particularly when the T/R ratio was 1.10 or 1.15. However, when the T/R ratio differed by 5%, from 1.0 to 1.05, the similarity between the \"original\" and \"sample\" decreased. It is noteworthy that the similarity percentage for the \"generated\" dataset compared with the \"original\" population consistently increased with each increment of the T/R ratio. The percent similarity reflects the success rate of the \(t\)-test evaluation, which is evident in the success percentages of the scenario with a = 1.0 and CV = 10%. These success rates are depicted in Figure 10 for all three sizes of the \"generated\" populations (i.e., 1000, 500, 100) and the two \"sample\" sizes (i.e., 50 and 100). Figure 8: Percent of success for the “original”, “sample”, and “generated” datasets when the size of the “sample” dataset is fixed at 100 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. Wasserstein distances were computed between the \"original\" and \"generated\" populations for both Test and Reference groups after each iteration. On average, these distances closely mirrored the product of the T/R ratio and the coefficient of variation. The Wasserstein distance, along with the similarity parameter, was considered to evaluate the degree of resemblance between the two distributions. In evaluating the performance of the WGAN and its susceptibility to variations in input data quality and diversity, our study maintained consistent WGAN hyperparameters across diverse scenarios. Notably, despite the introduction of variability in datasets containing varying patient numbers (ranging from 10,000 to 100), our findings indicate that the WGAN functioned effectively without necessitating a recalibration of the algorithmic parameters. This underscores the robustness of the WGAN architecture in accommodating diverse input data profiles, thereby showcasing its adaptability and reliability across a spectrum of clinical study settings.