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

Steven M. Shechter, Andrew J. Schaefer, R. Scott Braithwaite and Mark S. Roberts
Additional contact information
Steven M. Shechter: Department of Industrial Engineering, University of Pittsburgh, steven.shechter@sauder.ubc.edu
Andrew J. Schaefer: 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: Section of General Internal Medicine, Yale University School of Medicine
Mark S. Roberts: 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

Medical Decision Making, 2006, vol. 26, issue 5, 550-553

Abstract: 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.

Keywords: Monte Carlo cohort simulations; variance reduction techniques; estimation; policy comparison; common random numbers; antithetic variates (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:26:y:2006:i:5:p:550-553

DOI: 10.1177/0272989X06290489

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