## 4 Discussion  
This study presents a synthetic patient cohort generator designed for health modelling scenarios where complete patient-level data is not available. The method allows users to simulate realistic and clinically valid patient data using only summary statistics and a user-defined correlation matrix. The key advantage of this approach lies in its transparency and configurability: correlations are enforced directly, logical constraints are applied deterministically, and the generation process is fully auditable. The purpose of our proposed method significantly differs from generative models such as VAEs, GANs, Diffusion Models, or flow matching models, which require large, labeled datasets to learn latent representations of complex data distributions. These methods are not suitable for use in cases where real data is missing, sparse, or aggregated. Moreover, they do not support explicit constraint handling or guarantee the preservation of specific correlations. They inherently depend on learning distributions directly from existing data, implicitly assuming the availability of large and representative datasets; such an approach frequently fails in economic modeling, thus limiting the practicality of these methods when detailed patient-level data are not accessible. For example, ensuring that older age corresponds with higher systolic blood pressure or that only females over 40 can be postmenopausal would require extensive retraining or post-hoc filtering. In our method, these relationships are handled directly and transparently. Additionally, our study directly challenges the implicit but prevalent assumption in economic modeling practice that uncorrelated synthetic data sufficiently model disease progression. Our simulations, spanning five distinct scenarios across three datasets, clearly demonstrate that incorporating correlation structures--whether sourced from expert input or literature--consistently yields outputs more closely mirroring the intended population characteristics. Even approximate correlations substantially enhance plausibility compared to traditional uncorrelated approaches, reinforcing the practical value of explicitly modeled dependencies. In our study, we have not quantitatively compared our proposed method with generative models as such an evaluation would not be meaningful due to the fundamentally different purposes served by these methodologies. For example, while a GAN trained on comprehensive datasets such as Framingham may generate realistic patient records, this presupposes full data access and aims for realistic replication. In contrast, our method uniquely supports scenario-based simulations precisely when detailed patient data do not exist. Methodological trade-offs can be thus summarized as follows: generative models  
Figure 4: Simulated versus real correlation values across five input scenarios in the low-dimension population. Panels (**A**–**E**) display scatter plots comparing the correlation between pairs of variables from simulated data (\(y\)-axis) and real data (\(x\)-axis). The dashed diagonal line indicates perfect prediction. Panel (**A**): complete inputs; Panel (**B**): physician-estimated correlations; Panel (**C**): literature-based correlations; Panel (**D**): mixed inputs; Panel (**E**): no correlations. exel at producing realistic data from extensive sources but cannot readily enforce specific relationships. Conversely, our approach does not leverage learning from data but crucially allows precise, direct control over desired variable dependencies. A core advantage of the model is its ability to accurately match target values through an iterative mean adjustment process. This ensures that generated data stay within an acceptable range of the user-defined inputs, which is especially important in small-sample simulations where variability can easily skew the results.