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Markov cohort state-transition models have been the standard approach forsimulating the prognosis of patients or, more generally, the life trajectoriesof individuals over a time period. Current approaches for estimating thevariance of a Markov model using a Monte Carlo sampling or a master equationrepresentation are computationally expensive and analytically difficult toexpress and solve. We introduce an alternative representation of a Markov modelin the form of a multinomial distribution. We derive this representation fromprinciples and then verify its veracity in a simulation exercise. Thisrepresentation provides an exact and fast approach to compute the variance anda way to estimate transition probabilities in a Bayesian setting.

Markov cohort state-transition model: A multinomial distribution representation