Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling

Hawre Jalal, Jeremy D. Goldhaber-Fiebert, Karen M. Kuntz

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function - a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis, which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters.

Original languageEnglish (US)
Pages (from-to)584-595
Number of pages12
JournalMedical Decision Making
Volume35
Issue number5
DOIs
StatePublished - Jul 19 2015

Keywords

  • Bayesian statistical methods
  • cost-benefit analysis
  • probabilistic sensitivity analysis
  • simulation methods
  • value of information

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