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A Review of Methods for Analysis of the Expected Value of Information

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  • Anna Heath
  • Ioanna Manolopoulou
  • Gianluca Baio

Abstract

In recent years, value-of-information analysis has become more widespread in health economic evaluations, specifically as a tool to guide further research and perform probabilistic sensitivity analysis. This is partly due to methodological advancements allowing for the fast computation of a typical summary known as the expected value of partial perfect information (EVPPI). A recent review discussed some approximation methods for calculating the EVPPI, but as the research has been active over the intervening years, that review does not discuss some key estimation methods. Therefore, this paper presents a comprehensive review of these new methods. We begin by providing the technical details of these computation methods. We then present two case studies in order to compare the estimation performance of these new methods. We conclude that a method based on nonparametric regression offers the best method for calculating the EVPPI in terms of accuracy, computational time, and ease of implementation. This means that the EVPPI can now be used practically in health economic evaluations, especially as all the methods are developed in parallel with R functions and a web app to aid practitioners.

Suggested Citation

  • Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2017. "A Review of Methods for Analysis of the Expected Value of Information," Medical Decision Making, , vol. 37(7), pages 747-758, October.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:7:p:747-758
    DOI: 10.1177/0272989X17697692
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    References listed on IDEAS

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    2. Jeffrey M. Keisler & Zachary A. Collier & Eric Chu & Nina Sinatra & Igor Linkov, 2014. "Value of information analysis: the state of application," Environment Systems and Decisions, Springer, vol. 34(1), pages 3-23, March.
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    1. Gordon Hazen & Emanuele Borgonovo & Xuefei Lu, 2023. "Information Density in Decision Analysis," Decision Analysis, INFORMS, vol. 20(2), pages 89-108, June.
    2. Borgonovo, Emanuele & Hazen, Gordon B. & Jose, Victor Richmond R. & Plischke, Elmar, 2021. "Probabilistic sensitivity measures as information value," European Journal of Operational Research, Elsevier, vol. 289(2), pages 595-610.
    3. Hendrik Koffijberg & Claire Rothery & Kalipso Chalkidou & Janneke Grutters, 2018. "Value of Information Choices that Influence Estimates: A Systematic Review of Prevailing Considerations," Medical Decision Making, , vol. 38(7), pages 888-900, October.
    4. Ian Wadsworth & Lisa V. Hampson & Thomas Jaki & Graeme J. Sills & Anthony G. Marson & Richard Appleton, 2020. "A quantitative framework to inform extrapolation decisions in children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 515-534, February.
    5. Michael Drummond & Carlo Federici & Vivian Reckers‐Droog & Aleksandra Torbica & Carl Rudolf Blankart & Oriana Ciani & Zoltán Kaló & Sándor Kovács & Werner Brouwer, 2022. "Coverage with evidence development for medical devices in Europe: Can practice meet theory?," Health Economics, John Wiley & Sons, Ltd., vol. 31(S1), pages 179-194, September.
    6. Laura McCullagh & Susanne Schmitz & Michael Barry & Cathal Walsh, 2017. "Examining the Feasibility and Utility of Estimating Partial Expected Value of Perfect Information (via a Nonparametric Approach) as Part of the Reimbursement Decision-Making Process in Ireland: Applic," PharmacoEconomics, Springer, vol. 35(11), pages 1177-1185, November.
    7. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.

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