# Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models
## 4 Discussion  
Based on this hands-on modeling exercise, both CH and PL models are viable options for assessing the cost-effectiveness of alternative health technologies. A similar conclusion was reached by [PERSON] et al. [17] pertaining to modeling the metastatic melanoma condition. However, [PERSON] et al. utilized actual patient-level data from a clinical trial, showcasing the advantages of patient-level simulation (PLS) in capturing patient heterogeneity and treatment responses over time, while our study focused on the methodological trade-offs and computational considerations.  
Using both CH and PL techniques simultaneously with the same structure and assumptions necessitates additional time and resources from the modelers. However, this approach shifts the focus of validation and error detection to an earlier phase of development.  
The application of both [PERSON] modeling techniques revealed several similarities. The primary practical difference lies in the PL model's limited ability to analyze parameter uncertainty versus the CH model's limited flexibility for large-scale structural changes.  
Cross-validation using the 'heat map' proved effective in comparing patient pathways, aiding in the correction of computational and structural issues, and enhancing technical validity. However, the stochastic nature of the PL models may lead to misleading patterns in the 'heat map', particularly if the sample size is insufficient for robust results. Optimizing sample size with known techniques [18; 19] and simple algebra, such as the coefficient of variation, was useful in managing the computational burden of the PL models.  
While the CH model offers a stable foundation for model construction and simplifies the exploration of sensitive parameters and assumptions, the PL model provides greater flexibility for implementing changes with little effort to the original model, in line with the findings of [PERSON] et al. [17]. The programming skills required for both techniques are similar. Transparency is straightforward in the CH model, while it was only a matter of providing good intermediate outcomes on patient pathways for the PL model. Increased complexity presents similar data and input challenges for both model types. Consequently, a PL model may be preferred if the model is intended for unverified or multiple purposes. A CH model is often sufficient for a specific purpose with a clear understanding of the model's structure and features. Our study results were in line with [PERSON] et al. [17] regarding how the choice between CH and PL models depends on data availability, disease complexity, and the objectives of the evaluation.  
This study, like others, has its limitations. It focused on comparing two modeling techniques to support evaluating the cost-effectiveness of medication treatment options like in multiple sclerosis. In other contexts, factors such as the availability of resources, input data, disease complexity, model-building time, and specific model requirements may influence the choice of modeling technique. While some study outcomes were quantifiable, others were based on qualitative assessments, such as the analysts' opinions. The chosen modeling example was intentionally simple to align with the level of complexity of many health economic models. Increased complexity in models introduces additional technical and conceptual challenges, such as the non-linearity of events, population heterogeneity, timing, continuity, and interactions [6]. Future research should explore the external validation of both models, practical comparisons of various modeling methods, and decision problems related to different patient pathways and health policy areas. Additionally, exploring the integration of graphics processing units (GPUs) [20; 21] could significantly enhance computational efficiency and accelerated processing speed, particularly in PL models. Expandingthis research to evaluate the cost-effectiveness of novel imaging modalities [22] or other non-pharmaceutical interventions [23] could also be valuable.