Abstract
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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Acknowledgements
The authors gratefully acknowledge Drs. Karen Kuntz and Amy Knudsen for their advice and thoughtful reviews; Drs. Dennis Fryback and Marjorie Rosenberg and the entire University of Wisconsin breast cancer project team for the use of the breast cancer simulation model.
Financial disclosure
Dr. Stout was supported by the Agency for Healthcare Research and Quality Training Grant to the University of Wisconsin (HS00083 PI: Fryback), by the National Cancer Institute CISNET Consortium (CA88211 PI: Fryback) and by the Harvard Center for Risk Analysis. Dr. Goldie was supported in part by the Bill and Melinda Gates Foundation (30505) as well as the National Cancer Institute (R01 CA093435). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing and publishing the report.
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Stout, N.K., Goldie, S.J. Keeping the noise down: common random numbers for disease simulation modeling. Health Care Manage Sci 11, 399–406 (2008). https://doi.org/10.1007/s10729-008-9067-6
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DOI: https://doi.org/10.1007/s10729-008-9067-6