Discrete event simulation (DES) is a computer-modelling technique used in economic evaluation of health interventions in which individual patient experience is simulated over time, and events occurring to the patient and the consequences of such events are tracked and summarised. Unlike cohort Markov models, in DES movements between patients’ health states are usually driven by events which may occur at varying times (rather than during cycles of fixed length), and time-to-event distributions are required for each event. Life courses of events and health states are constructed for a succession of individual modelled patients, which may then be aggregated over time to produce the summary experience of a patient cohort. Event likelihoods are driven by individual patient characteristics, which are recorded at baseline and may be updated as the patient experience (events, new health states) accumulates. Events and health states can be associated with resource use/cost and utilities. DES is likely to be useful for modelling complex conditions with many possible types of event and health state (e.g. complications of diabetes) or situations where the patient’s history may impact on future events. The building up of individual patient histories in DES gives this method some attraction especially to clinician reviewers. However the modelling can become more complex than more straightforward techniques (e.g. cohort Markov) and deriving time-to-event input values and distributions can be challenging.


How to cite: Discrete Event Simulation [online]. (2016). York; York Health Economics Consortium; 2016. https://yhec.co.uk/glossary/discrete-event-simulation/


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