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Medical Decision Making, Vol. 26, No. 5, 550-553 (2006)
DOI: 10.1177/0272989X06290489
© 2006 Society for Medical Decision Making

Increasing the Efficiency of Monte Carlo Cohort Simulations with Variance Reduction Techniques

Steven M. Shechter, MS

Department of Industrial Engineering, University of Pittsburgh, steven.shechter{at}sauder.ubc.edu

Andrew J. Schaefer, PhD

Department of Industrial Engineering, University of Pittsburgh, Center for Research on Health Care, University of Pittsburgh, Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine

R. Scott Braithwaite, MD

Section of General Internal Medicine, Yale University School of Medicine

Mark S. Roberts, MD, MPP

Department of Industrial Engineering, University of Pittsburgh, Center for Research on Health Care, University of Pittsburgh, Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine

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.

Key Words: Monte Carlo cohort simulations • variance reduction techniques • estimation • policy comparison • common random numbers • antithetic variates


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