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 < 0.1% change in demand) as the evidence base became established.
Conclusions
Evidence of external validity was found. Demand was weak and responsive to price in the near term because of uncertainty and an immature evidence base. Clear communication of precision medicine outcomes and uncertainty is crucial in allowing healthcare to align with individual preferences.
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Data Availability Statement
Subject to the appropriate ethics clearance, the dataset generated for the study is available from authors on reasonable request.
References
Ginsburg GS, Phillips KA. Precision medicine: from science to value. Health Aff (Millwood). 2018;37(5):694–701.
Feero WG, Wicklund CA, Veenstra D. Precision medicine, genome sequencing, and improved population health. JAMA. 2018;319(19):1979–80.
Manolio TA, Chisholm RL, Ozenberger B, Roden DM, Williams MS, Wilson R, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med. 2013;15(4):258–67.
Roberts MC, Kennedy AE, Chambers DA, Khoury MJ. The current state of implementation science in genomic medicine: opportunities for improvement. Genet Med. 2017;19(8):858–63.
Regier DA, Weymann D, Buchanan J, Marshall DA, Wordsworth S. Valuation of health and nonhealth outcomes from next-generation sequencing: approaches, challenges, and solutions. Value Health. 2018;21(9):1043–7.
Knowles L, Luth W, Bubela T. Paving the road to personalized medicine: recommendations on regulatory, intellectual property and reimbursement challenges. J Law Biosci. 2017;4(3):453–506.
Hunter DJ. Uncertainty in the era of precision medicine. N Engl J Med. 2016;375(8):711–3.
Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care. The economics of non-market goods and resources. Dordrecht: Springer; 2007.
Bridges JF, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, et al. Conjoint analysis applications in health—a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14(4):403–13.
Coast J, Al-Janabi H, Sutton EJ, Horrocks SA, Vosper AJ, Swancutt DR, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ. 2012;21(6):730–41.
Reed Johnson F, Lancsar E, Marshall D, Kilambi V, Muhlbacher A, Regier DA, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health. 2013;16(1):3–13.
Lancaster K. A new approach to consumer theory. J Polit Econ. 1966;74:132–57.
McFadden D. conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers in econometrics. New York: Academic Press; 1974. p. 105–42.
Strauss AL. Qualitative analysis for social scientists. Cambridge: Cambridge University Press; 1987.
Ozdemir S, Johnson FR, Hauber AB. Hypothetical bias, cheap talk, and stated willingness to pay for health care. J Health Econ. 2009;28(4):894–901.
Revelt D, Train K. Mixed logit with repeated choices: households’ choices of appliance efficiency level. Rev Econ Stat. 1998;80(4):647–57.
Daly A, Dekker T, Hess S. Dummy coding vs effects coding for categorical variables: clarifications and extensions. J Choice Model. 2016;21:36–41.
Carlson JJ, Roth JA. The impact of the Oncotype Dx breast cancer assay in clinical practice: a systematic review and meta-analysis. Breast Cancer Res Treat. 2013;141(1):13–22.
Hornberger J, Chien R, Krebs K, Hochheiser L. US insurance program’s experience with a multigene assay for early-stage breast cancer. J Oncol Pract. 2011;7(3 Suppl):e38s–45s.
Brownstone D, Train K. Forecasting new product penetration with flexible substitution patterns. J Econom. 1999;89(1–2):109–29.
McFadden D, Train K. Mixed MNL models for discrete response. J Appl Econom. 2000;15(5):447–70.
Small KA, Rosen HS. Applied welfare economics with discrete choice models. Econometrica. 1981;49(1):105–30.
Health G. Investor relations. Genomic Health. https://investor.genomichealth.com/investor-relations. Accessed 05 Jun 2017.
SEER*Stat software version 8.3.5. [database on the Internet]2017. Available from: www.seer.cancer.gov/seerstat. Accessed 22 Mar 2018.
Howlader N, Altekruse SF, Li CI, Chen VW, Clarke CA, Ries LA et al. US incidence of breast cancer subtypes defined by joint hormone receptor and HER2 status. J Natl Cancer Inst. 2014;106(5):1–8.
Ringel JS, United States Department of Defence. Office of the Secretary of Defence, National Defence Research Institute (U.S.), RAND Health. The elasticity of demand for health care: a review of the literature and its application to the military health system. Santa Monica: RAND; 2002.
Lancsar E, Swait J. Reconceptualising the external validity of discrete choice experiments. Pharmacoeconomics. 2014;32(10):951–65.
Quaife M, Terris-Prestholt F, Di Tanna GL, Vickerman P. How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity. Eur J Health Econ. 2018;19(8):1053–66.
Regier DA, Diorio C, Ethier MC, Alli A, Alexander S, Boydell KM, et al. Discrete choice experiment to evaluate factors that influence preferences for antibiotic prophylaxis in pediatric oncology. PLoS One. 2012;7(10):e47470.
Najafzadeh M, Johnston KM, Peacock SJ, Connors JM, Marra MA, Lynd LD, et al. Genomic testing to determine drug response: measuring preferences of the public and patients using discrete choice experiment (DCE). BMC Health Serv Res. 2013;13:454.
Buchanan J, Wordsworth S, Schuh A. Patients’ preferences for genomic diagnostic testing in chronic lymphocytic leukaemia: a discrete choice experiment. Patient. 2016;9(6):525–36.
Acknowledgements
The authors thank Dr. Jagori Saha for assistance in conducting the discrete-choice experiment.
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All authors participated in the concept and design of the study, data acquisition and interpretation, and contributed to drafting, revising, and final approval of the manuscript. Regier and Carlson conducted the analysis and take responsibility for the integrity of the data and data analysis.
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This work was supported by the National Institutes of Health (NIH) Common Fund project 1U01AG047109. The funding agreement ensures the independence of the authors in all aspects of study design and data interpretation and in writing and publishing the article.
Conflict of interest
Dr. Regier has received travel support from Illumina to attend conferences in Boston, USA, and Barcelona, Spain. Dr. Veenstra has provided paid consultancy to Roche Sequencing Systems. Drs. Carlson and Basu have no conflicts of interest that are directly relevant to the content of this article.
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The University of Washington Institutional Review Board granted research ethics to conduct this project.
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Informed consent was obtained from all individual participants included in the study.
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Regier, D.A., Veenstra, D.L., Basu, A. et al. Demand for Precision Medicine: A Discrete-Choice Experiment and External Validation Study. PharmacoEconomics 38, 57–68 (2020). https://doi.org/10.1007/s40273-019-00834-0
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DOI: https://doi.org/10.1007/s40273-019-00834-0