## 4 Discussion
Future developments could include systematic approaches like Bayesianestimation or expert consensus methodologies (e.g., Delphi technique) to partially address these scenarios. In our future study, we will address the handling of nonlinear relationships through two distinct methodological approaches. First, we will employ a stratification strategy using the Framingham dataset, targeting correlations that notably change direction or magnitude before and after age 40 in females, such as cardiovascular risk profile factors (e.g., cholesterol levels or blood pressure). The dataset will be stratified into two subgroups--females younger than 40 and those aged 40 or older. We will then separately generate a synthetic cohort for each subgroup before merging them into a combined dataset. Comparative analyses will be performed by calculating metrics for each subgroup individually and for the merged dataset against the original data, explicitly evaluating the method's capability to accurately capture these reversed correlations. Secondly, we will utilize a linked-equation approach using waist circumference data from NHANES, calculated deterministically from BMI and age using validated clinical formulas. We will compare synthetic cohorts generated with and without deterministic linking, assessing accuracy using statistical metrics such as the mean absolute error (MAE), root mean square error (RMSE), sum of squared errors (SSE), and R2. This evaluation will quantify the improvements in realism and accuracy achieved by employing explicit linking equations. In cases where waist circumference will exhibit linearity, alternative nonlinear clinical equations, such as the Framingham cardiovascular risk scores or renal function equations (MDRD, Cockcroft-Gault), will be identified and tested to further validate the robustness of the linked-equation approach in capturing nonlinear clinical relationships. Our study aligns with the growing use of synthetic data in healthcare, where access to real patient data is often limited by ethical, regulatory, and institutional constraints. As argued by [PERSON] et al. [23], synthetic datasets provide a privacy-preserving alternative by replicating the statistical properties of real populations, enabling cross-institutional research, algorithm development, and educational simulations without compromising sensitive information. With the increasing adoption of AI in healthcare, simulators capable of generating large-scale, diverse, and clinically coherent data are becoming critical for training, validation, and testing, especially in contexts of data sparsity or population-specific modeling needs. Beyond technical applications, synthetic data could support broader goals such as data-driven policy development [8]. However, clinical adoption remains cautious due to concerns about data quality, bias, re-identification risk, and provider trust [8]. Regulatory oversight, along with safeguards such as Differential Privacy [24] and dataset provenance tracking [25], will be essential to ensure ethical and responsible deployment. While we do not compare our method quantitatively against GANs or VAEs in this study, their required inputs and modeling goals differ substantially. These models aim to discover latent features from raw data and replicate its statistical properties in aggregate. Our method instead enables scenario-based modeling in the absence of patient-level data, which is a common requirement in early-stage evaluations, feasibility studies, and constrained research settings.