## 1 Introduction  
The use of synthetic patient data in medical research is rapidly gaining popularity due to its ability to model large, complex multivariate datasets that replicate actual features while maintaining patient privacy [1]. Large-scale patient datasets play a crucial role in applications such as clinical trial simulations and modeling disease progression, allowing decisionmakers to make evidence-based decisions. The validity of such models relies not only on the mathematical structure of the simulation but also on the realism of the patient cohort being simulated [2]. A highly sophisticated simulation model cannot produce meaningful results if the underlying patient dataset does not accurately reflect real-world patient characteristics [3]. While actual datasets from electronic health records (EHRs), clinical trials, and population studies exist, they are often expensive, difficult to access, incomplete, or lack the essential patient characteristics required for economic modeling. Furthermore, missing data remains a persistent issue, requiring techniques such as imputation or omission, though both approaches carry inherent limitations and risk introducing bias [4; 5]. A promising alternative to actual real-world data is the generation of synthetic patient cohorts. However, simple random data generation is insufficient, as it fails to account for interdependencies between variables or enforce biological plausibility. For example, in a realistic dataset, older age should correlate with higher blood pressure, and biologically implausible cases, such as pregnant male patients or pediatric smokers, must be prevented. For a synthetic patient dataset to be useful in modeling, it must replicate not only the marginal distributions of individual characteristics but also the correlations and clinical constraints between multiple variables, ensuring that relationships observed in real-world data are preserved [6]. Most existing methods generate synthetic data without explicit control over inter-variable relationships, which may result in implausible patient profiles. Deep-learning-based approaches often require large, high-quality real datasets for training and struggle to accurately reproduce the complex, high-dimensional relationships present in patient data, especially when data are scarce or privacy-restricted [7]. These models are prone to amplifying biases present in the original data, which can lead to unbalanced or non-representative synthetic cohorts and potentially discriminatory outcomes [7; 8]. Furthermore, deep generative models typically operate as \"black boxes,\" offering limited transparency and interpretability regarding how variable dependencies are established or how clinical logic is enforced [8]. Additionally, many generative models are computationally intensive, requiring significant resources for training and sampling, which can be a barrier in practical healthcare settings [7; 9]. Even methods that use statistical resampling or copulas often have difficulty simultaneously preserving marginal distributions and realistic inter-variable correlations, particularly when only summary statistics are available. To address these challenges, a patient cohort generator is needed to create large-scale, realistic synthetic datasets that preserve the underlying statistical properties of the real population. Such a tool must generate patient characteristics that follow real-world statistical distributions while enforcing biological plausibility and preserving meaningful correlations between variables. Although significant research has been conducted on synthetic data generation across various domains including image, text, and tabular data, the complexity, heterogeneity, and incompleteness of real-world patient datasets poses significant challenges in constructing generative models that produce clinically useful synthetic data [10; 11; 12].