{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/markov-cohort-state-transition-model-a","title":"Markov cohort state-transition model: A multinomial distribution representation","arxiv_id":"2204.02805","date":"2022-04-06","proceeding":null,"authors":["Rowan Iskandar","Cassandra Berns"],"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 compute the variance and a way to estimate transition probabilities in a Bayesian setting.","url_abs":"https://arxiv.org/abs/2204.02805v1","url_pdf":"https://arxiv.org/pdf/2204.02805v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"markov-cohort-state-transition-model-a","repo_url":"https://github.com/rowaniskandar/cm_multinomial","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}