Keeping the noise down: common random numbers for disease simulation modeling

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.