IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v36y2016i3p285-295.html
   My bibliography  Save this article

Interpretation of the Expected Value of Perfect Information and Research Recommendations

Author

Listed:
  • Joanna Thorn
  • Joanna Coast
  • Lazaros Andronis

Abstract

Background. Expected value of perfect information (EVPI) calculations are increasingly performed to guide and underpin research recommendations. An EVPI value that exceeds the estimated cost of research forms a necessary (although not sufficient) condition for further research to be considered worthwhile. However, it is unclear what factors affect researchers’ recommendations and whether there is a notional threshold of positive returns below which research is not recommended. The objectives of this study were to explore whether EVPI and other factors have a bearing on research recommendations and to assess whether there exists a threshold EVPI below which research is typically not recommended. Methods. A systematic literature review was undertaken to identify applied EVPI calculations in the health care field. Study characteristics were extracted, including funder, location, disease group, publication year, primary language, and outcome measure. Population EVPI values and willingness-to-pay thresholds were also extracted alongside verbatim text excerpts describing the authors’ research recommendations. Recommendations were classified according to whether further research was recommended (a positive recommendation) or not (negative). Factors affecting the likelihood of a positive recommendation were examined statistically using logistic regression and visually by plotting the results in graphs. Results and Conclusions. Eighty-six articles were included, of which 13 suggested no further research, 66 recommended further research, and 7 gave no recommendation. EVPI appears to be a key driver of researchers’ recommendations for further research. Disease area, funder, study location, publication year, and outcome may have a bearing on recommendations, although none of these factors reached statistical significance. A threshold EVPI value below which research is typically not recommended was found at around £1.48 million.

Suggested Citation

  • Joanna Thorn & Joanna Coast & Lazaros Andronis, 2016. "Interpretation of the Expected Value of Perfect Information and Research Recommendations," Medical Decision Making, , vol. 36(3), pages 285-295, April.
  • Handle: RePEc:sae:medema:v:36:y:2016:i:3:p:285-295
    DOI: 10.1177/0272989X15586552
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X15586552
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X15586552?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
    ---><---

    References listed on IDEAS

    as
    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. Karl Claxton & John Posnett, 1996. "An economic approach to clinical trial design and research priority‐setting," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 513-524, November.
    3. Drummond, Michael F. & Davies, Linda M. & Ferris, Frederick L., 1992. "Assessing the costs and benefits of medical research: The diabetic retinopathy study," Social Science & Medicine, Elsevier, vol. 34(9), pages 973-981, May.
    4. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    Full references (including those not matched with items on IDEAS)

    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. Lazaros Andronis & Lucinda J. Billingham & Stirling Bryan & Nicholas D. James & Pelham M. Barton, 2016. "A Practical Application of Value of Information and Prospective Payback of Research to Prioritize Evaluative Research," Medical Decision Making, , vol. 36(3), pages 321-334, April.
    2. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    3. Josh J. Carlson & Rahber Thariani & Josh Roth & Julie Gralow & N. Lynn Henry & Laura Esmail & Pat Deverka & Scott D. Ramsey & Laurence Baker & David L. Veenstra, 2013. "Value-of-Information Analysis within a Stakeholder-Driven Research Prioritization Process in a US Setting: An Application in Cancer Genomics," Medical Decision Making, , vol. 33(4), pages 463-471, May.
    4. Hawre Jalal & Jeremy D. Goldhaber-Fiebert & Karen M. Kuntz, 2015. "Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling," Medical Decision Making, , vol. 35(5), pages 584-595, July.
    5. Jennifer Uyei & R. Scott Braithwaite, 2016. "Are There Scenarios When the Use of Non–Placebo-Control Groups in Experimental Trial Designs Increase Expected Value to Society?," Medical Decision Making, , vol. 36(1), pages 20-30, January.
    6. Manuel A. Espinoza & Andrea Manca & Karl Claxton & Mark J. Sculpher, 2014. "The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis," Medical Decision Making, , vol. 34(8), pages 951-964, November.
    7. A C Bouman & A J ten Cate-Hoek & B L T Ramaekers & M A Joore, 2015. "Sample Size Estimation for Non-Inferiority Trials: Frequentist Approach versus Decision Theory Approach," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    8. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    9. Neil Hawkins & Mark Sculpher & David Epstein, 2005. "Cost-Effectiveness Analysis of Treatments for Chronic Disease: Using R to Incorporate Time Dependency of Treatment Response," Medical Decision Making, , vol. 25(5), pages 511-519, September.
    10. Mark Strong & Jeremy E. Oakley, 2013. "An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information," Medical Decision Making, , vol. 33(6), pages 755-766, August.
    11. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 6," Medical Decision Making, , vol. 33(5), pages 671-678, July.
    12. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.
    13. Sassi, Franco, 2003. "Setting priorities for the evaluation of health interventions: when theory does not meet practice," Health Policy, Elsevier, vol. 63(2), pages 141-154, February.
    14. Eric Jutkowitz & Fernando Alarid-Escudero & Hyon K. Choi & Karen M. Kuntz & Hawre Jalal, 2017. "Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis," PharmacoEconomics, Springer, vol. 35(10), pages 1073-1085, October.
    15. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.
    16. Linda Davies & Mike Drummond & Panos Papanikoloau, 1999. "Prioritising investments in health technology assessment: can we assess the potential value for money?," Working Papers 170chedp, Centre for Health Economics, University of York.
    17. Mark Strong & Jeremy E. Oakley & Alan Brennan & Penny Breeze, 2015. "Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 35(5), pages 570-583, July.
    18. Fleurence, Rachael L. & Torgerson, David J., 2004. "Setting priorities for research," Health Policy, Elsevier, vol. 69(1), pages 1-10, July.
    19. Mark Strong & Jeremy E. Oakley & Alan Brennan, 2014. "Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 34(3), pages 311-326, April.
    20. Jeff Miller & Rolf Ulrich, 2019. "The quest for an optimal alpha," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.

    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:sae:medema:v:36:y:2016:i:3:p:285-295. 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: SAGE Publications (email available below). General contact details of provider: .

    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.