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A patient-level simulation model represents outcomes by simulating individual patients one at a time, allowing each patient's characteristics and accumulated history to determine the risks, events, costs and outcomes they subsequently experience. This individual-level approach captures heterogeneity and history-dependence that averaged cohort models cannot, making it appropriate where prognosis depends on the sequence or timing of past events, on continuous risk factors, or on interactions that vary between patients. Its flexibility is offset by greater demands for data, computation and validation, and by the need to simulate many patients to obtain stable estimates. It encompasses approaches such as micro-simulation and discrete event simulation and is used where such realism materially affects the estimated costs and effects.

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