Increasing the efficiency of Monte Carlo cohort simulations with variance reduction techniques.
The authors discuss techniques for Monte Carlo (MC) cohort simulations that reduce the number of simulation replications required to achieve a given degree of precision for various output measures. Known as variance reduction techniques, they are often used in industrial engineering and operations research models, but they are seldom used in medical models. However, most MC cohort simulations are well suited to the implementation of these techniques. The authors discuss the cost of implementation versus the benefit of reduced replications.
Shechter
Steven M
SM
Department of Industrial Engineering, University of Pittsburgh, USA. steven.shechter@sauder.ubc.edu
Schaefer
Andrew J
AJ
Braithwaite
R Scott
RS
Roberts
Mark S
MS
D003198:Computer Simulation / Q000706:statistics & numerical data*; D003657:Decision Making; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method