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From the same journal

Strategies for efficient computation of the expected value of partial perfect information

Research output: Contribution to journalArticle

Published copy (DOI)

Author(s)

  • Jason Madan
  • Anthony E Ades
  • Malcolm Price
  • Kathryn Maitland
  • Julie Jemutai
  • Paul Revill
  • Nicky J Welton

Department/unit(s)

Publication details

JournalMedical Decision Making
DateE-pub ahead of print - 21 Jan 2014
DatePublished (current) - 2014
Issue number3
Volume34
Number of pages16
Pages (from-to)1-16
Early online date21/01/14
Original languageEnglish

Abstract

Expected value of information methods evaluate the potential health benefits that can be obtained from conducting new research to reduce uncertainty in the parameters of a cost-effectiveness analysis model, hence reducing decision uncertainty. Expected value of partial perfect information (EVPPI) provides an upper limit to the health gains that can be obtained from conducting a new study on a subset of parameters in the cost-effectiveness analysis and can therefore be used as a sensitivity analysis to identify parameters that most contribute to decision uncertainty and to help guide decisions around which types of study are of most value to prioritize for funding. A common general approach is to use nested Monte Carlo simulation to obtain an estimate of EVPPI. This approach is computationally intensive, can lead to significant sampling bias if an inadequate number of inner samples are obtained, and incorrect results can be obtained if correlations between parameters are not dealt with appropriately. In this article, we set out a range of methods for estimating EVPPI that avoid the need for nested simulation: reparameterization of the net benefit function, Taylor series approximations, and restricted cubic spline estimation of conditional expectations. For each method, we set out the generalized functional form that net benefit must take for the method to be valid. By specifying this functional form, our methods are able to focus on components of the model in which approximation is required, avoiding the complexities involved in developing statistical approximations for the model as a whole. Our methods also allow for any correlations that might exist between model parameters. We illustrate the methods using an example of fluid resuscitation in African children with severe malaria.

Bibliographical note

©2014 The Author(s)

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