Discrete Event Simulation (DES) 

Discrete Event Simulation (DES) is a sophisticated computer-modelling technique used in the economic evaluation of health interventions. It simulates the individual patient experience over time, tracking and summarizing the events that occur and their consequences. Unlike cohort Markov models, where transitions between health states happen at fixed intervals, DES allows events to occur at variable times, requiring time-to-event distributions for each possible event.

In DES, the life courses of events and health states are constructed for a series of individual patients. These individual experiences are then aggregated to produce the overall summary for a patient cohort. The likelihood of events occurring is driven by specific patient characteristics recorded at baseline and updated as new events and health states develop. Each event and health state can be associated with resource use, costs, and utilities.

DES is particularly useful for modeling complex conditions with numerous potential events and health states, such as the complications associated with diabetes. It is also beneficial in scenarios where a patient’s history significantly influences future events. The detailed individual patient histories built up in DES make it especially appealing to clinicians who review the models.

However, DES can be more complex than other modeling techniques like cohort Markov models. The complexity arises from the need to derive time-to-event input values and distributions accurately, which can be challenging. Despite this, the ability of DES to handle variability and provide detailed simulations makes it a powerful tool in health economics, particularly for conditions requiring nuanced and individualized modeling.

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