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Identifying key parameters in cost‐effectiveness analysis using value of information: a comparison of methods

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  • Bas Groot Koerkamp
  • M. G. Myriam Hunink
  • Theo Stijnen
  • Milton C. Weinstein

Abstract

Decisions in health care must be made, despite uncertainty about benefits, risks, and costs. Value of information analysis is a theoretically sound method to estimate the expected value of future quantitative research pertaining to an uncertain decision. If the expected value of future research does not exceed the cost of research, additional research is not justified, and decisions should be based on current evidence, despite the uncertainty. To assess the importance of individual parameters relevant to a decision, different value of information methods have been suggested. The generally recommended method assumes that the expected value of perfect knowledge concerning a parameter is estimated as the reduction in expected opportunity loss. This method, however, results in biased expected values and incorrect importance ranking of parameters. The objective of this paper is to set out the correct methods to estimate the partial expected value of perfect information and to demonstrate why the generally recommended method is incorrect conceptually and mathematically. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Bas Groot Koerkamp & M. G. Myriam Hunink & Theo Stijnen & Milton C. Weinstein, 2006. "Identifying key parameters in cost‐effectiveness analysis using value of information: a comparison of methods," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 383-392, April.
    • Handle: RePEc:wly:hlthec:v:15:y:2006:i:4:p:383-392
    DOI: 10.1002/hec.1064
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    References listed on IDEAS

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    Cited by:

    1. Haitham Tuffaha & Shelley Roberts & Wendy Chaboyer & Louisa Gordon & Paul Scuffham, 2015. "Cost-Effectiveness and Value of Information Analysis of Nutritional Support for Preventing Pressure Ulcers in High-risk Patients: Implement Now, Research Later," Applied Health Economics and Health Policy, Springer, vol. 13(2), pages 167-179, April.
    2. 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.
    3. Gordon Hazen & Emanuele Borgonovo & Xuefei Lu, 2023. "Information Density in Decision Analysis," Decision Analysis, INFORMS, vol. 20(2), pages 89-108, June.
    4. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    5. Andrew H. Briggs & Milton C. Weinstein & Elisabeth A. L. Fenwick & Jonathan Karnon & Mark J. Sculpher & A. David Paltiel, 2012. "Model Parameter Estimation and Uncertainty Analysis," Medical Decision Making, , vol. 32(5), pages 722-732, September.
    6. Galioto, F., 2018. "The value of information for the management of water resources in agriculture: comparing the economic impact of alternative sources of information to schedule irrigation," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277384, International Association of Agricultural Economists.
    7. Dirk Müller & Eleanor Pullenayegum & Afschin Gandjour, 2015. "Impact of small study bias on cost-effectiveness acceptability curves and value of information analyses," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 219-223, March.
    8. Galioto, Francesco & Chatzinikolaou, Parthena & Raggi, Meri & Viaggi, Davide, 2020. "The value of information for the management of water resources in agriculture: Assessing the economic viability of new methods to schedule irrigation," Agricultural Water Management, Elsevier, vol. 227(C).
    9. Williams, Byron K. & Eaton, Mitchell J. & Breininger, David R., 2011. "Adaptive resource management and the value of information," Ecological Modelling, Elsevier, vol. 222(18), pages 3429-3436.

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