Markov Cohort State-Transition Model: A Multinomial Distribution Representation

Rowan Iskandar and Cassandra Berns
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Rowan Iskandar: Center of Excellence in Decision-Analytic Modeling and Health Economics Research, sitem-insel, Bern, Switzerland
Cassandra Berns: Center of Excellence in Decision-Analytic Modeling and Health Economics Research, sitem-insel, Bern, Switzerland

Medical Decision Making, 2023, vol. 43, issue 1, 139-142

Abstract: Markov cohort state-transition models have been the standard approach for simulating the prognosis of patients or, more generally, the life trajectories of individuals over a time period. Current approaches for estimating the variance of a Markov model using a Monte Carlo sampling or a master equation representation are computationally expensive and analytically difficult to express and solve. We introduce an alternative representation of a Markov model in the form of a multinomial distribution. We derive this representation from principles and then verify its veracity in a simulation exercise. This representation provides an exact and fast approach to computing the variance and a way of estimating transition probabilities in a Bayesian setting. Highlights A Markov model simulates the average experience of a cohort of patients. Monte Carlo simulation, the standard approach for estimating the variance, is computationally expensive. A multinomial distribution provides an exact representation of a Markov model. Using the known formulas of a multinomial distribution, the mean and variance of a Markov model can be readily calculated.

Keywords: cohort model; cost-effectiveness analysis; decision-analytic model; markov model; state-transition model (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:1:p:139-142

DOI: 10.1177/0272989X221112420

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