Sensitivity analysis and the expected value of perfect information

James C. Felli*, Gordon B. Hazen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

207 Scopus citations


Measures of decision sensitivity that have been applied to medical decision problems were examined. Traditional threshold proximity methods have recently been supplemented by probabilistic sensitivity analysis, and by entropy-based measures of sensitivity. The authors propose a fourth measure based upon the expected value of perfect information (EVPI), which they believe superior both methodologically and pragmatically. Both the traditional and the newly suggested sensitivity measures focus entirety on the likelihood of decision change without attention to corresponding changes in payoff, which are often small. Consequently, these measures can dramatically overstate 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 measures.

Original languageEnglish (US)
Pages (from-to)95-109
Number of pages15
JournalMedical Decision Making
Issue number1
StatePublished - 1998


  • Expected value of perfect information
  • Sensitivity analysis

ASJC Scopus subject areas

  • Health Policy


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