Keeping the Noise Down: Common Random Numbers for Disease Simulation Modeling

Program in Health Decision Science, Harvard School of Public Health, 718 Huntington Ave., Boston, MA 02115, USA.
Health Care Management Science (Impact Factor: 1.05). 01/2009; 11(4):399-406. DOI: 10.1007/s10729-008-9067-6
Source: PubMed


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.

Full-text preview

Available from:
  • Source
    • "To determine osteoporosis-attributable fracture costs (excess costs), the difference in costs between the two considered risk groups was calculated [17]. To eliminate the stochastic noise between the two groups (with regard to simulated costs and fracture numbers), that is the randomly generated difference between the two groups because of using different random streams (sequence of random numbers) for each group, the same random stream (common random numbers) was used for both simulated groups [39]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Osteoporotic fractures cause a large health burden and substantial costs. This study estimated the expected fracture numbers and costs for the remaining lifetime of postmenopausal women in Germany. Methods A discrete event simulation (DES) model which tracks changes in fracture risk due to osteoporosis, a previous fracture or institutionalization in a nursing home was developed. Expected lifetime fracture numbers and costs per capita were estimated for postmenopausal women (aged 50 and older) at average osteoporosis risk (AOR) and for those never suffering from osteoporosis. Direct and indirect costs were modeled. Deterministic univariate and probabilistic sensitivity analyses were conducted. Results The expected fracture numbers over the remaining lifetime of a 50 year old woman with AOR for each fracture type (% attributable to osteoporosis) were: hip 0.282 (57.9%), wrist 0.229 (18.2%), clinical vertebral 0.206 (39.2%), humerus 0.147 (43.5%), pelvis 0.105 (47.5%), and other femur 0.033 (52.1%). Expected discounted fracture lifetime costs (excess cost attributable to osteoporosis) per 50 year old woman with AOR amounted to €4,479 (€1,995). Most costs were accrued in the hospital €1,743 (€751) and long-term care sectors €1,210 (€620). Univariate sensitivity analysis resulted in percentage changes between -48.4% (if fracture rates decreased by 2% per year) and +83.5% (if fracture rates increased by 2% per year) compared to base case excess costs. Costs for women with osteoporosis were about 3.3 times of those never getting osteoporosis (€7,463 vs. €2,247), and were markedly increased for women with a previous fracture. Conclusion The results of this study indicate that osteoporosis causes a substantial share of fracture costs in postmenopausal women, which strongly increase with age and previous fractures.
    Full-text · Article · Jun 2014 · BMC Health Services Research

  • No preview · Chapter · Jan 2012
  • Source
    [Show abstract] [Hide abstract]
    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.
    Preview · Article · Sep 2012 · Medical Decision Making
Show more