State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–3
Abstract
State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs.
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Within-cycle correction is an approach to reducing the biases introduced by discretization in discrete-time cSTMs.
“Another approach is to use within-cycle corrections (WCC).2,22”
From An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example (2023) · cites this paper as [2]
Cohort state-transition models (cSTMs) are usually computationally less demanding than individual-based state-transition models (iSTMs), providing the ability to conduct probabilistic sensitivity analysis (PSA) and value-of-information (VOI) analyses that otherwise might not be computationally feasible with iSTMs.
“cSTMs are usually computationally less demanding than individual-based state-transition models (iSTMs), providing the ability to conduct PSA and value-of-information (VOI) analyses that otherwise might not be computationally feasible with iSTMs.2”
From An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example (2023) · cites this paper as [2]
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The Half-Cycle Correction Revisited
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All MCIDs Are Wrong, But Some May be Useful
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Economic Evaluation of Extended-Release Buprenorphine for Persons With Opioid Use Disorder
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Cost-Effectiveness Models in Breast Cancer Screening in the General Population: A Systematic Review
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Self-citation
- Authors
- Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M.
- Affiliations
- St. Michael's Hospital (CA)
- UMIT–University for Health Sciences, Medical Informatics and Technology,Hall/Tyrol, Austria (US)
- Venue
- Medical Decision Making
- Publisher
- SAGE Publications
- Published
- 2012-09
- Volume
- 32
- Issue
- 5
- Pages
- 690-700
- ISSN
- 0272-989X (ISSN-L)
- 1552-681X
- OpenAlex
- W2132708603
- Cited by
- 348
- References
- 59
- Citations by year
- 2026: 23 · 2025: 23 · 2024: 19 · 2023: 18 · 2022: 28 · 2021: 23 · 2020: 29 · 2019: 25 · 2018: 24 · 2017: 29 · 2016: 25 · 2015: 32 · 2014: 21 · 2013: 19 · 2012: 8
- FWCI
- 61.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Decision Making (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- state transition modeling, Monte Carlo simulation, clinical decision analysis, health technology assessment, health economic evaluation, health states, risk factor intervention, screening, diagnostic procedures, treatment options, disease management programs
- MeSH terms
- Decision Making, Markov Chains, Models, Theoretical, Cohort Studies, Benchmarking
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