Can Discrete Event Simulation be of Use in Modeling Major Depression?
2006
This article, published in Cost Effectiveness and Resource Allocation, reviews the published literature on Markov models in depression and identified potential limitations in using this particular modelling approach in this disease area. Additionally, the authors develop a “Discrete Event Simulation” (DES) model to investigate the benefits and drawbacks of this simulation method compared with Markov modelling techniques.
The findings of this study indicate that the most important limitation of using Markov models in depression is tracking patients' disease history properly, which cannot be done unless the analyst defines multiple health states. A second drawback of Markov models is the rigidity when considering multiple patient-specific sociodemographic characteristics in a single model, which would require defining multiple health states and a complex model. DES models, in contrast, resolve these weaknesses and allow patients with differing attributes to move from one event to another in a sequential order while simultaneously taking into account important risk factors such as age, gender, disease history and patients' attitudes towards treatment, together with any disease-related events (adverse events, suicide attempt, etc.).
Based on their findings the authors conclude that DES models seem to be particularly useful in modeling recurrent and chronic diseases compared with Markov models due to the accurate, flexible and comprehensive means of depicting disease progression.
Can Discrete Event Simulation be of Use in Modeling Major Depression?
Source:
Le Lay A, Despiegel N, François C et al. Can Discrete Event Simulation be of Use in Modelling Major Depression? Cost Effectiveness and Resource Allocation 2006; 4 (19). https://doi.org/10.1186/1478-7547-4-19