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Sensitivity Analysis and the Expected Value of Perfect Information

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  • James C. Felli
  • Gordon B. Hazen

Abstract

Measures of decision sensitivity that have been applied to medical decision problems were examined. Traditional threshold proximity methods have recently been supple mented by probabilistic sensitivity analysis, and by entropy-based measures of sen sitivity. The authors propose a fourth measure based upon the expected value of perfect information (EVPI), which they believe superior both methodologically and prag matically. Both the traditional and the newly suggested sensitivity measures focus en tirely on the likelihood of decision change without attention to corresponding changes in payoff, which are often small. Consequently, these measures can dramatically over state problem sensitivity. EVPI, on the other hand, incorporates both the probability of a decision change and the marginal benefit of such a change into a single measure, and therefore provides a superior picture of problem sensitivity. To lend support to this contention, the authors revisit three problems from the literature and compare the results of sensitivity analyses using probabilistic, entropy-based, and EVPI-based mea sures. Key words: sensitivity analysis; expected value of perfect information. (Med Decis Making 1998;18:95-109)

Suggested Citation

  • James C. Felli & Gordon B. Hazen, 1998. "Sensitivity Analysis and the Expected Value of Perfect Information," Medical Decision Making, , vol. 18(1), pages 95-109, January.
    • Handle: RePEc:sae:medema:v:18:y:1998:i:1:p:95-109
    DOI: 10.1177/0272989X9801800117
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    References listed on IDEAS

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    1. Peter C. Fishburn & Allan H. Murphy & Herbert H. Isaacs, 1968. "Sensitivity of Decisions to Probability Estimation Errors: A Reexamination," Operations Research, INFORMS, vol. 16(2), pages 254-267, April.
    2. 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.
    3. David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
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