Keeping the noise down: common random numbers for disease simulation modeling
Open accessAbstract
Disease simulation models are used to conduct decision analyses of the comparative benefits and risks associated with preventive and treatment strategies. To address increasing model complexity and computational intensity, modelers use variance reduction techniques to reduce stochastic noise and improve computational efficiency. One technique, common random numbers, further allows modelers to conduct counterfactual-like analyses with direct computation of statistics at the individual level. This technique uses synchronized random numbers across model runs to induce correlation in model output thereby making differences easier to distinguish as well as simulating identical individuals across model runs. We provide a tutorial introduction and demonstrate the application of common random numbers in an individual-level simulation model of the epidemiology of breast cancer.
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Variance reduction techniques (e.g., using common random numbers) can decrease the required numbers of individuals.
“Variance reduction techniques (e.g., using common random numbers) can decrease the required numbers [56].”
From State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3 (2012) · cites this paper as [56]
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Research works printed but not linked: 21
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Cited by
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State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.014 · Cited by 495 · see citing works
State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–3
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. (2012) · Medical Decision Making · doi:10.1177/0272989x12455463 · Cited by 348 · see citing works
- Authors
- Stout, N. K., & Goldie, S. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Stout, Natasha K.
- ORCID
- Stout, Natasha K.
- Venue
- Health Care Management Science
- Published
- 2008-04-11
- Volume
- 11
- Issue
- 4
- Pages
- 399-406
- ISSN
- 1386-9620 (ISSN-L)
- 1572-9389
- PMC
- PMC2761656
- OpenAlex
- W2029004113
- Cited by
- 40
- References
- 0
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 1 · 2022: 3 · 2021: 5 · 2020: 2 · 2019: 3 · 2018: 1 · 2017: 5 · 2016: 1 · 2015: 2 · 2014: 3 · 2013: 1 · 2012: 3
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Health Care Management Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Agency for Healthcare Research and Quality (T32HS000083, HS00083, HS000083, HS000083-12, F32 HS000083)
- National Cancer Institute (F32CA125984, R01CA093435, U01CA088211, CA88211)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- 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 › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- common random numbers, variance reduction, disease simulation model, stochastic noise, individual-level simulation, counterfactual analysis, breast cancer epidemiology, decision analysis, computational efficiency
- MeSH terms
- Breast Neoplasms, Female, Humans, Mammography, Models, Statistical
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