## 4 Discussion
The model also handles correlation structures effectively using Cholesky decomposition, a stable and efficient method that ensures that the correlation matrix is mathematically valid, even with many variables. Moreover, by resolving parameters in a dependency-aware sequence (e.g., calculating BMI before blood pressure), the generator helps maintain clinical plausibility in simulated data [13]. Importantly, this study indicates that incorporating correlations between variables, whether drawn from expert estimates or published literature, substantially improves the quality of the simulated cohorts. In real-world research, where full datasets may be incomplete or unavailable due to privacy concerns, relying on these estimates is often a practical and preferable solution. Our results clearly demonstrate that preserving correlation structure is crucial: the \"no correlation\" scenario consistently produced the least realistic outputs across all population types. This emphasizes that adding some form of nearly precise correlation is more effective than assuming that all variables are independent [20]. Another key insight from the validation results is the high performance of expert-informed correlations in comparison to literature-derived matrices. Expert input often outperformed literature-based or mixed matrices, particularly in cardiac and low-dimensional cohorts. This suggests that population-specific clinical insight may offer greater accuracy than generalized published correlations. In contrast, the literature-based matrices, especially when derived from different populations such as Chinese datasets applied to NHANES cohort generation (an American population), sometimes performed worse than using no correlations at all [17; 18]. This indicates the critical importance of population-specific context and emphasizes the risk of introducing bias or unrealistic relationships when applying mismatched assumptions in synthetic modeling. Although our results indicate that physician-estimated correlations often outperformed literature-derived ones, this finding requires cautious interpretation. In our study, physician estimates were averaged from two clinicians, likely reducing individual biases. Notably, despite our experts originating from a different country than the patient data sources, their estimates still demonstrated strong performance, highlighting the usefulness of structured expert input. Inter-rater reliability results reflected good agreement between clinicians, with slightly tighter agreement observed in the Framingham dataset, possibly due to differences in data familiarity, complexity, or variability of the variables assessed. To further enhance consistency, clinical experts were provided with a general framework, future applications may benefit from tailoring this framework to specific modeling contexts. It is also conceivable that literature-based correlations could surpass expert estimates if derived from closely matched populations. However, in practice, such high-quality, population-specific correlations are seldom available, especially in settings characterized by data scarcity or population diversity. Therefore, while acknowledging the potential superiority of literature-based inputs under ideal conditions, expert-derived correlations often represent a more feasible and adaptable choice for real-world modeling applications. Deep generative models may perform well when trained on large, homogeneous datasets from a single population, but their generalizability in health economic applications is often limited. By contrast, our method supports flexible population assumptions, is agnostic to the data source, and can incorporate either empirical or elicited correlation structures with equal ease. Many existing methods for synthetic data generation prioritize either privacy or realism, often sacrificing one for the other. For example, Data Synthesizer uses differential privacy to protect sensitive information, but this can reduce the variability and utility of the synthetic data for real-world analysis.