2022/04/06 by Iskandar, Rowan, Berns, Cassandra
#Applications (stat.AP) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2204.02805
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 compute the variance and a way to estimate transition probabilities in a Bayesian setting.