Modeling Complex Treatment Strategies: Construction and Validation of a Discrete Event Simulation Model for Glaucoma
Article first published online: 30 JUN 2010
© 2009, International Society for Pharmacoeconomics and Outcomes Research (ISPOR)
Value in Health
Volume 13, Issue 4, pages 358–367, June/July 2010
How to Cite
Van Gestel, A., Severens, J. L., Webers, C. A. B., Beckers, H. J. M., Jansonius, N. M. and Schouten, J. S. A. G. (2010), Modeling Complex Treatment Strategies: Construction and Validation of a Discrete Event Simulation Model for Glaucoma. Value in Health, 13: 358–367. doi: 10.1111/j.1524-4733.2009.00678.x
- Issue published online: 30 JUN 2010
- Article first published online: 30 JUN 2010
- discrete event simulation;
- disease-progression model;
- ocular hypertension;
- primary open-angle glaucoma;
Objective: Discrete event simulation (DES) modeling has several advantages over simpler modeling techniques in health economics, such as increased flexibility and the ability to model complex systems. Nevertheless, these benefits may come at the cost of reduced transparency, which may compromise the model's face validity and credibility. We aimed to produce a transparent report on the construction and validation of a DES model using a recently developed model of ocular hypertension and glaucoma.
Methods: Current evidence of associations between prognostic factors and disease progression in ocular hypertension and glaucoma was translated into DES model elements. The model was extended to simulate treatment decisions and effects. Utility and costs were linked to disease status and treatment, and clinical and health economic outcomes were defined. The model was validated at several levels. The soundness of design and the plausibility of input estimates were evaluated in interdisciplinary meetings (face validity). Individual patients were traced throughout the simulation under a multitude of model settings to debug the model, and the model was run with a variety of extreme scenarios to compare the outcomes with prior expectations (internal validity). Finally, several intermediate (clinical) outcomes of the model were compared with those observed in experimental or observational studies (external validity) and the feasibility of evaluating hypothetical treatment strategies was tested.
Results: The model performed well in all validity tests. Analyses of hypothetical treatment strategies took about 30 minutes per cohort and lead to plausible health–economic outcomes.
Conclusion: There is added value of DES models in complex treatment strategies such as glaucoma. Achieving transparency in model structure and outcomes may require some effort in reporting and validating the model, but it is feasible.