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

Authors
  • Jalal, Hawre1, 2
  • Goldhaber-Fiebert, Jeremy D2
  • Kuntz, Karen M3
  • 1 Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California (HJ)
  • 2 Center for Health Policy/Center for Primary Care & Outcomes Research, School of Medicine, Stanford University, Stanford, California (HJ, JDGF)
  • 3 Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis (KMK)
Type
Published Article
Journal
Medical decision making : an international journal of the Society for Medical Decision Making
Publication Date
Jul 01, 2015
Volume
35
Issue
5
Pages
584–595
Identifiers
DOI: 10.1177/0272989X15578125
PMID: 25840900
Source
Medline
Keywords
License
Unknown

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.

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