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Demand for Precision Medicine: A Discrete-Choice Experiment and External Validation Study

Author

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  • Dean A. Regier

    (BC Cancer
    University of British Columbia)

  • David L. Veenstra

    (University of Washington)

  • Anirban Basu

    (University of Washington)

  • Josh J. Carlson

    (University of Washington)

Abstract

Background A limited evidence base and lack of clear clinical guidelines challenge healthcare systems’ adoption of precision medicine. The effect of these conditions on demand is not understood. Objective This research estimated the public’s preferences and demand for precision medicine outcomes. Methods A discrete-choice experiment survey was conducted with an online sample of the US public who had recent healthcare experience. Statistical analysis was undertaken using an error components mixed logit model. The responsiveness of demand in the context of a changing evidence base was estimated through the price elasticity of demand. External validation was examined using real-world demand for the 21-gene recurrence score assay for breast cancer. Results In total, 1124 (of 1849) individuals completed the web-based survey. The most important outcomes were survival gains with statistical uncertainty, cost of testing, and medical expert agreement on changing care based on test results. The value ($US, year 2017 values) for a test where most (vs. few) experts agreed to changing treatment based on test results was $US1100 (95% confidence interval [CI] 916–1286). Respondents were willing to pay $US265 (95% CI 46–486) for a test that could result in greater certainty around life-expectancy gains. The predicted demand of the assay was 9% in 2005 and 66% in 2014, compared with real-world uptake of 7% and 71% (root-mean-square prediction error 0.11). Demand was sensitive to price (1% increase in price resulted in > 1% change in demand) when first introduced and insensitive to price (1% increase in price resulted in

Suggested Citation

  • Dean A. Regier & David L. Veenstra & Anirban Basu & Josh J. Carlson, 2020. "Demand for Precision Medicine: A Discrete-Choice Experiment and External Validation Study," PharmacoEconomics, Springer, vol. 38(1), pages 57-68, January.
    • Handle: RePEc:spr:pharme:v:38:y:2020:i:1:d:10.1007_s40273-019-00834-0
    DOI: 10.1007/s40273-019-00834-0
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    References listed on IDEAS

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    3. Office of Health Economics, 2007. "The Economics of Health Care," For School 001490, Office of Health Economics.
    4. Matthew Quaife & Fern Terris-Prestholt & Gian Luca Di Tanna & Peter Vickerman, 2018. "How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1053-1066, November.
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    Cited by:

    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. John Buckell & Vrinda Vasavada & Sarah Wordsworth & Dean A. Regier & Matthew Quaife, 2022. "Utility maximization versus regret minimization in health choice behavior: Evidence from four datasets," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 363-381, February.

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