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Value-of-Information Analysis within a Stakeholder-Driven Research Prioritization Process in a US Setting: An Application in Cancer Genomics

Author

Listed:
  • Josh J. Carlson
  • Rahber Thariani
  • Josh Roth
  • Julie Gralow
  • N. Lynn Henry
  • Laura Esmail
  • Pat Deverka
  • Scott D. Ramsey
  • Laurence Baker
  • David L. Veenstra

Abstract

Objective . The objective of this study was to evaluate the feasibility and outcomes of incorporating value-of-information (VOI) analysis into a stakeholder-driven research prioritization process in a US-based setting. Methods . Within a program to prioritize comparative effectiveness research areas in cancer genomics, over a period of 7 months, we developed decision-analytic models and calculated upper-bound VOI estimates for 3 previously selected genomic tests. Thirteen stakeholders representing patient advocates, payers, test developers, regulators, policy makers, and community-based oncologists ranked the tests before and after receiving VOI results. The stakeholders were surveyed about the usefulness and impact of the VOI findings. Results . The estimated upper-bound VOI ranged from $33 million to $2.8 billion for the 3 research areas. Seven stakeholders indicated the results modified their rankings, 9 stated VOI data were useful, and all indicated they would support its use in future prioritization processes. Some stakeholders indicated expected value of sampled information might be the preferred choice when evaluating specific study designs. Limitations . Our study was limited by the size and the potential for selection bias in the composition of the external stakeholder group, lack of a randomized design to assess effect of VOI data on rankings, and the use of expected value of perfect information v. expected value of sample information methods. Conclusions . Value of information analyses may have a meaningful role in research topic prioritization for comparative effectiveness research in the United States, particularly when large differences in VOI across topic areas are identified. Additional research is needed to facilitate the use of more complex value of information analyses in this setting.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:medema:v:33:y:2013:i:4:p:463-471
    DOI: 10.1177/0272989X13484388
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. Karl Claxton & Simon Eggington & Laura Ginnelly & Susan Griffin & Christopher McCabe & Zoe Philips & Paul Tappenden & Alan Wailoo, 2005. "A Pilot Study of Value of Information Analysis to Support Research Recommendations for the National Institute for Health and Clinical Excellence," Working Papers 004cherp, Centre for Health Economics, University of York.
    3. 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.
    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.
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    1. Veenstra David L. & Mandelblatt Jeanne & Neumann Peter & Basu Anirban & Peterson Josh F. & Ramsey Scott D., 2020. "Health Economics Tools and Precision Medicine: Opportunities and Challenges," Forum for Health Economics & Policy, De Gruyter, vol. 23(1), pages 1-14, June.
    2. Katharina Fischer & Reiner Leidl, 2014. "Analysing coverage decision-making: opening Pandora’s box?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(9), pages 899-906, December.
    3. Hester V Eeren & Saskia J Schawo & Ron H J Scholte & Jan J V Busschbach & Leona Hakkaart, 2015. "Value of Information Analysis Applied to the Economic Evaluation of Interventions Aimed at Reducing Juvenile Delinquency: An Illustration," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    4. David Glynn & Georgios Nikolaidis & Dina Jankovic & Nicky J. Welton, 2023. "Constructing Relative Effect Priors for Research Prioritization and Trial Design: A Meta-epidemiological Analysis," Medical Decision Making, , vol. 43(5), pages 553-563, July.

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