The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
GRADE guidelines: 3. Rating the quality of evidence
Balshem, H., Helfand, M., Schünemann, H. J., Oxman, A. D., Kunz, R., Brozek, J., Vist, G. E., Falck-Ytter, Y., Meerpohl, J., & Norris, S. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.07.015 · Cited by 8,285
Puterman, M. L. (1994) · Wiley series in probability and statistics · doi:10.1002/9780470316887 · Cited by 6,057
GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology
Guyatt, G. H., Oxman, A. D., Schünemann, H. J., Tugwell, P., & Knottnerus, A. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.09.011 · Cited by 3,094
Markov Models in Medical Decision Making: A Practical Guide
Sonnenberg, F. A., & Beck, J. R. (1993) · Medical Decision Making · doi:10.1177/0272989x9301300409 · Cited by 2,419
The Markov process in medical prognosis
Beck, J. R., & Pauker, S. G. (1983) · Medical Decision Making · doi:10.1177/0272989x8300300403 · Cited by 957
Briggs, A. H., Weinstein, M. C., Fenwick, E. A. L., Karnon, J., Sculpher, M. J., & Paltiel, A. D. (2012) · Medical Decision Making · doi:10.1177/0272989x12458348 · Cited by 887
Model Transparency and Validation
Eddy, D. M., Hollingworth, W., Jaime, J., Tsevat, J., McDonald, K. M., & Wong, J. B. (2012) · Medical Decision Making · doi:10.1177/0272989x12454579 · Cited by 803
Briggs, A. H., Weinstein, M. C., Fenwick, E. A. L., Karnon, J., Sculpher, M. J., & Paltiel, A. D. (2012) · Value in Health · doi:10.1016/j.jval.2012.04.014 · Cited by 711
Clarke, P. M., Gray, A. M., Briggs, A., Farmer, A. J., Fenn, P., Stevens, R. J., Matthews, D. R., Stratton, I. M., Holman, R. R., & on behalf of the UK Prospective Diabetes Study (UKPDS) Group (2004) · Diabetologia · doi:10.1007/s00125-004-1527-z · Cited by 615
Cost-Effectiveness of Screening for HIV in the Era of Highly Active Antiretroviral Therapy
Sanders, G. D., Bayoumi, A. M., H, V. S. M. P., Bilir, S. P., Neukermans, C. P., Rydzak, C. E., Douglass, L. R., Lazzeroni, L. C., Holodniy, M., & Owens, D. K. (2005) · New England Journal of Medicine · doi:10.1056/nejmsa042657 · Cited by 603
Self-citation
Research works printed but not linked: 2
- Robins JM, 2004, Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Vol. 1, 2191
- Sonnenberg FA, 1994, Med Care, 32, JS52
Cited by
Sorted by citations
Husereau, D., Drummond, M., Petrou, S., Carswell, C., Moher, D., Greenberg, D., Augustovski, F., Briggs, A. H., Mauskopf, J., & Loder, E. (2013) · Value in Health · doi:10.1016/j.jval.2013.02.002 · Cited by 1,998
Model Transparency and Validation
Eddy, D. M., Hollingworth, W., Jaime, J., Tsevat, J., McDonald, K. M., & Wong, J. B. (2012) · Medical Decision Making · doi:10.1177/0272989x12454579 · Cited by 803
Briggs, A. H., Weinstein, M. C., Fenwick, E. A. L., Karnon, J., Sculpher, M. J., & Paltiel, A. D. (2012) · Medical Decision Making · doi:10.1177/0272989x12458348 · Cited by 887
Briggs, A. H., Weinstein, M. C., Fenwick, E. A. L., Karnon, J., Sculpher, M. J., & Paltiel, A. D. (2012) · Value in Health · doi:10.1016/j.jval.2012.04.014 · Cited by 711
Modeling infectious disease dynamics in the complex landscape of global health
Heesterbeek, H., Anderson, R. M., Andreasen, V., Bansal, S., Angelis, D. D., Dye, C., Eames, K. T. D., Edmunds, W. J., Frost, S. D. W., Funk, S., et al. (2015) · Science · doi:10.1126/science.aaa4339 · Cited by 721
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
