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A Markov model is a state-transition model that represents a condition as a set of mutually exclusive and collectively exhaustive health states, through which a cohort moves over a series of equal time cycles according to specified transition probabilities, accumulating costs and health outcomes as it goes. Its defining assumption, that the probability of moving from one state to another depends only on the current state and not on the history of how it was reached, is known as the Markovian property, and where memory of past events matters this can be addressed by adding states or using individual-level simulation. Markov models are well suited to chronic conditions with recurring events over long horizons and are among the most widely used structures in cost-effectiveness analysis. Their credibility depends on a clinically plausible state structure, well-evidenced transition probabilities, appropriate cycle length and half-cycle correction, and validation of their outputs.

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