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A Systematic Review of Microsimulation Models in Cardiometabolic Disease: Model Calibration and Validation
Danwei Yang1, Zhixin Cao1, Jayoung Byun2
1Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
Applied Health Economics and Health Policy
|March 25, 2026
Summary
Reporting of calibration and validation in cardiometabolic disease microsimulation models has improved, yet gaps persist. Future work should focus on rigorous application-based model development, clearer reporting, and increased use of open-source practices for transparency.
Area of Science:
- Health economics and outcomes research
- Computational epidemiology
- Biostatistics
Background:
- Microsimulation models are vital for projecting health trajectories in cardiometabolic diseases.
- Guidelines exist for model calibration and validation, but adherence in practice is unclear.
Purpose of the Study:
- To assess the reporting of calibration and validation in cardiometabolic disease microsimulation models.
- To examine variations in reporting practices based on study characteristics.
Main Methods:
- Systematic review of microsimulation models of cardiometabolic diseases (2016-2024).
- Assessment of reporting adherence to established calibration and validation guidelines.
- Analysis of variations by model type, disease scope, and open-source status.
Main Results:
- 31 studies were included; 52% focused on application-based models, 48% on natural history models.
- 74% reported calibration processes (targets, parameters); 84% reported validation (external validation most common).
- Predictive validation was not reported; natural history models and open-sourced code showed better reporting.
Conclusions:
- While reporting has improved, significant gaps in calibration and validation persist.
- Recommendations include enhancing application-based model rigor, improving reporting clarity, and promoting open-source practices.
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