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Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies

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Listed:
  • Anna Heath

    (The Hospital for Sick Children, Toronto, ON, Canada
    University of Toronto, Toronto, ON, Canada
    University College London, London, UK)

  • Natalia Kunst

    (Department of Health Management and Health Economics, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
    Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, Yale University School of Medicine and Yale Cancer Center, New Haven, CT, USA
    Department of Epidemiology and Biostatistics, Amsterdam UMC, Amsterdam, the Netherlands
    LINK Medical Research, Oslo, Norway)

  • Christopher Jackson

    (MRC Biostatistics Unit, University of Cambridge, Cambridge, UK)

  • Mark Strong

    (School of Health and Related Research, University of Sheffield, Sheffield, UK)

  • Fernando Alarid-Escudero

    (Center for Research and Teaching in Economics (CIDE))

  • Jeremy D. Goldhaber-Fiebert

    (Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA)

  • Gianluca Baio

    (University College London, London, UK)

  • Nicolas A. Menzies

    (Harvard T. H. Chan School of Public Health, Boston, MA, USA)

  • Hawre Jalal

    (University of Pittsburgh, Pittsburgh, PA, USA)

Abstract

Background. Investing efficiently in future research to improve policy decisions is an important goal. Expected value of sample information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Recently, several more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared, and therefore their comparative performance across different examples has not been explored. Methods. We compared 4 EVSI methods using 3 previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared. Results. In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. Conclusions. As all the evaluated methods gave estimates similar to those given by traditional Monte Carlo, we suggest that EVSI can now be efficiently computed with confidence in realistic examples. No systematically superior EVSI computation method exists as the properties of the different methods depend on the underlying health economic model, data generation process, and user expertise.

Suggested Citation

  • Anna Heath & Natalia Kunst & Christopher Jackson & Mark Strong & Fernando Alarid-Escudero & Jeremy D. Goldhaber-Fiebert & Gianluca Baio & Nicolas A. Menzies & Hawre Jalal, 2020. "Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies," Medical Decision Making, , vol. 40(3), pages 314-326, April.
  • Handle: RePEc:sae:medema:v:40:y:2020:i:3:p:314-326
    DOI: 10.1177/0272989X20912402
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    References listed on IDEAS

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    1. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225, November.
    2. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
    3. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Mathyn Vervaart & Mark Strong & Karl P. Claxton & Nicky J. Welton & Torbjørn Wisløff & Eline Aas, 2022. "An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial," Medical Decision Making, , vol. 42(5), pages 612-625, July.
    2. Anna Heath, 2022. "Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation," Medical Decision Making, , vol. 42(5), pages 626-636, July.
    3. Nichola R. Naylor & Jack Williams & Nathan Green & Felicity Lamrock & Andrew Briggs, 2023. "Extensions of Health Economic Evaluations in R for Microsoft Excel Users: A Tutorial for Incorporating Heterogeneity and Conducting Value of Information Analyses," PharmacoEconomics, Springer, vol. 41(1), pages 21-32, January.
    4. Haitham Tuffaha & Claire Rothery & Natalia Kunst & Chris Jackson & Mark Strong & Stephen Birch, 2021. "A Review of Web-Based Tools for Value-of-Information Analysis," Applied Health Economics and Health Policy, Springer, vol. 19(5), pages 645-651, September.

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