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Probabilistic Sensitivity Analysis Incorporating the Bootstrap

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  • David J. Pasta
  • Jennifer L. Taylor
  • James M. Henning

Abstract

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distri butions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity. Key words : decision analysis; sensitivity analysis; decision trees; bootstrap; Monte Carlo; cost-effective ness ; sampling. (Med Decis Making 1999; 19:353-363)

Suggested Citation

  • David J. Pasta & Jennifer L. Taylor & James M. Henning, 1999. "Probabilistic Sensitivity Analysis Incorporating the Bootstrap," Medical Decision Making, , vol. 19(3), pages 353-363, August.
    • Handle: RePEc:sae:medema:v:19:y:1999:i:3:p:353-363
    DOI: 10.1177/0272989X9901900314
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    References listed on IDEAS

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    1. Gregory C. Critchfield & Keith E. Willard, 1986. "Probabilistic Analysis of Decision Trees Using Monte Carlo Simulation," Medical Decision Making, , vol. 6(2), pages 85-92, June.
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    1. Karnon, Jonathan, 2002. "Planning the efficient allocation of research funds: an adapted application of a non-parametric Bayesian value of information analysis," Health Policy, Elsevier, vol. 61(3), pages 329-347, September.
    2. Thomas Delea & Paul Tappenden & Oleg Sofrygin & Dominy Browning & Mayur Amonkar & Jon Karnon & Mel Walker & David Cameron, 2012. "Cost-effectiveness of lapatinib plus capecitabine in women with HER2+ metastatic breast cancer who have received prior therapy with trastuzumab," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(5), pages 589-603, October.
    3. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
    4. James C. Felli & Gordon B. Hazen, 2004. "Javelin Diagrams: A Graphical Tool for Probabilistic Sensitivity Analysis," Decision Analysis, INFORMS, vol. 1(2), pages 93-107, June.
    5. David J. Vanness & W. Ray Kim, 2002. "Bayesian estimation, simulation and uncertainty analysis: the cost‐effectiveness of ganciclovir prophylaxis in liver transplantation," Health Economics, John Wiley & Sons, Ltd., vol. 11(6), pages 551-566, September.

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