IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v35y2017i11d10.1007_s40273-017-0552-y.html
   My bibliography  Save this article

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: Application to Drugs for Cancer

Author

Listed:
  • Laura McCullagh

    (Trinity College Dublin
    St James’s Hospital)

  • Susanne Schmitz

    (St James’s Hospital
    Luxembourg Institute of Health)

  • Michael Barry

    (Trinity College Dublin
    St James’s Hospital)

  • Cathal Walsh

    (University of Limerick)

Abstract

Background In Ireland, all new drugs for which reimbursement by the healthcare payer is sought undergo a health technology assessment by the National Centre for Pharmacoeconomics. The National Centre for Pharmacoeconomics estimate expected value of perfect information but not partial expected value of perfect information (owing to computational expense associated with typical methodologies). Objective The objective of this study was to examine the feasibility and utility of estimating partial expected value of perfect information via a computationally efficient, non-parametric regression approach. Methods This was a retrospective analysis of evaluations on drugs for cancer that had been submitted to the National Centre for Pharmacoeconomics (January 2010 to December 2014 inclusive). Drugs were excluded if cost effective at the submitted price. Drugs were excluded if concerns existed regarding the validity of the applicants’ submission or if cost-effectiveness model functionality did not allow required modifications to be made. For each included drug (n = 14), value of information was estimated at the final reimbursement price, at a threshold equivalent to the incremental cost-effectiveness ratio at that price. The expected value of perfect information was estimated from probabilistic analysis. Partial expected value of perfect information was estimated via a non-parametric approach. Input parameters with a population value at least €1 million were identified as potential targets for research. Results All partial estimates were determined within minutes. Thirty parameters (across nine models) each had a value of at least €1 million. These were categorised. Collectively, survival analysis parameters were valued at €19.32 million, health state utility parameters at €15.81 million and parameters associated with the cost of treating adverse effects at €6.64 million. Those associated with drug acquisition costs and with the cost of care were valued at €6.51 million and €5.71 million, respectively. Conclusion This research demonstrates that the estimation of partial expected value of perfect information via this computationally inexpensive approach could be considered feasible as part of the health technology assessment process for reimbursement purposes within the Irish healthcare system. It might be a useful tool in prioritising future research to decrease decision uncertainty.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:pharme:v:35:y:2017:i:11:d:10.1007_s40273-017-0552-y
    DOI: 10.1007/s40273-017-0552-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40273-017-0552-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40273-017-0552-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Laura McCullagh & Michael Barry, 2016. "The Pharmacoeconomic Evaluation Process in Ireland," PharmacoEconomics, Springer, vol. 34(12), pages 1267-1276, December.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Suaad Almajed & Nora Alotaibi & Sana Zulfiqar & Zahraa Dhuhaibawi & Niall O’Rourke & Richard Gaule & Caoimhe Byrne & Aaron M. Barry & Dylan Keeley & James F. O’Mahony, 2022. "Cost-effectiveness evidence on approved cancer drugs in Ireland: the limits of data availability and implications for public accountability," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(3), pages 375-431, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gordon Hazen & Emanuele Borgonovo & Xuefei Lu, 2023. "Information Density in Decision Analysis," Decision Analysis, INFORMS, vol. 20(2), pages 89-108, June.
    2. 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.
    3. 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.
    4. Suaad Almajed & Nora Alotaibi & Sana Zulfiqar & Zahraa Dhuhaibawi & Niall O’Rourke & Richard Gaule & Caoimhe Byrne & Aaron M. Barry & Dylan Keeley & James F. O’Mahony, 2022. "Cost-effectiveness evidence on approved cancer drugs in Ireland: the limits of data availability and implications for public accountability," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(3), pages 375-431, April.
    5. 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.
    6. 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.
    7. Paul K. Gorecki, 2017. "Availability and Pricing New Medicines in Ireland: Reflections and Reform," PharmacoEconomics, Springer, vol. 35(10), pages 981-987, October.
    8. Felicity Lamrock & Laura McCullagh & Lesley Tilson & Michael Barry, 2020. "A retrospective analysis of budget impact models submitted to the National Centre for Pharmacoeconomics in Ireland," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(6), pages 895-901, August.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pharme:v:35:y:2017:i:11:d:10.1007_s40273-017-0552-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.