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Cited by 2,419Health Systems, Economic Evaluations, Quality of LifeClinical practice guidelines implementationSimulation Techniques and Applications

Markov Models in Medical Decision Making: A Practical Guide

Sonnenberg, Frank A., Beck, J. Robert·1993·Medical Decision Making
DOI 10.1177/0272989x9301300409
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Abstract

Markov models are useful when a decision problem involves risk that is continuous over time, when the timing of events is important, and when important events may happen more than once. Representing such clinical settings with conventional decision trees is difficult and may require unrealistic simplifying assumptions. Markov models assume that a patient is always in one of a finite number of discrete health states, called Markov states. All events are represented as transitions from one state to another. A Markov model may be evaluated by matrix algebra, as a cohort simulation, or as a Monte Carlo simulation. A newer representation of Markov models, the Markov-cycle tree, uses a tree representation of clinical events and may be evaluated either as a cohort simulation or as a Monte Carlo simulation. The ability of the Markov model to represent repetitive events and the time dependence of both probabilities and utilities allows for more accurate representation of clinical settings that involve these issues.

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  • The cohort and individual-based state-transition frameworks focused on in this report do not capture interactions, model a single closed cohort, and allow transitions to occur only at specified time intervals.

    “These frameworks do not capture interactions, model a single (closed) cohort, and allow transitions to occur only at specified time intervals.”

    From State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3 (2012) · cites this paper as [8]

  • Giving a full reward at the start (i.e., transitions occur at cycle end) overestimates expected values; assigning no reward (i.e., transitions occur at cycle start) underestimates them.

    “Giving a full reward at the start (i.e., transitions occur at cycle end) overestimates expected values; assigning no reward (i.e., transitions occur at cycle start) underestimates them [8].”

    From State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3 (2012) · cites this paper as [8]

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Authors
Sonnenberg, F. A., & Beck, J. R.
ORCID
Venue
Medical Decision Making
Publisher
SAGE Publications
Published
1993-12
Volume
13
Issue
4
Pages
322-338
ISSN
  • 0272-989X (ISSN-L)
  • 1552-681X
DOI
10.1177/0272989x9301300409
OpenAlex
W2164654252
Cited by
2,419
References
1
Citations by year
2026: 60 · 2025: 46 · 2024: 50 · 2023: 47 · 2022: 85 · 2021: 86 · 2020: 90 · 2019: 69 · 2018: 89 · 2017: 76 · 2016: 87 · 2015: 97 · 2014: 96 · 2013: 124 · 2012: 91
FWCI
48.86
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Medical Decision Making (publishedVersion)
  • PubMed (publishedVersion)
  • Rutgers University Community Repository (Rutgers University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • National Institutes of Health (5r29lm005266-05)
  • Agency for Healthcare Research and Quality (HS06396)
  • U.S. National Library of Medicine (LM05266)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
Keywords
Markov model, health states, state transitions, cohort simulation, Monte Carlo simulation, time-dependent probabilities, decision tree
MeSH terms
Adult, Anticoagulants, Decision Support Techniques, Embolism, Heart Valve Prosthesis, Hemorrhage, Humans, Male, Markov Chains, Monte Carlo Method, Postoperative Complications, Prognosis, Quality of Life, Cohort Studies, Survival Rate, Kidney Transplantation

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