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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Steven M. Shechter & Andrew J. Schaefer & R. Scott Braithwaite & Mark S. Roberts, 2006. "Increasing the Efficiency of Monte Carlo Cohort Simulations with Variance Reduction Techniques," Medical Decision Making, , vol. 26(5), pages 550-553, September.
    • Handle: RePEc:sae:medema:v:26:y:2006:i:5:p:550-553
    DOI: 10.1177/0272989X06290489
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    Cited by:

    1. Hendriek Boshuizen & Stefan Lhachimi & Pieter Baal & Rudolf Hoogenveen & Henriette Smit & Johan Mackenbach & Wilma Nusselder, 2012. "The DYNAMO-HIA Model: An Efficient Implementation of a Risk Factor/Chronic Disease Markov Model for Use in Health Impact Assessment (HIA)," Demography, Springer;Population Association of America (PAA), vol. 49(4), pages 1259-1283, November.

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