Is the Choice of Cost-Effectiveness Threshold in Cost-Utility Analysis Endogenous to the Resulting Value of Technology? A Systematic Review

Abstract

Background

Cost-utility analysis (CUA) is widely used for health technology assessment; however, concerns exist that cost-utility analysts may suggest higher cost-effectiveness thresholds (CETs) to compensate for technologies of relatively lower value.

Objective

We explored whether selection of a CUA study’s CET was endogenous to estimated incremental cost-effectiveness ratios (ICERs).

Methods

We systematically reviewed the US cost-effectiveness literature between 2000 and 2017 where studies with explicit CET and ICERs were included. We classified the ratio of studies hypothesized to analyze cost-effective technologies at low CETs (i.e., less than $100,000/quality-adjusted life-year [QALY]) vs higher CETs (i.e., $100,000–$150,000/QALY) relative to their ICER, using a Chi square test to examine whether technologies that were cost effective at high CETs would still be cost effective at lower thresholds. We also performed fixed-effects linear regression exploring the associations between ICERs and reported CETs over time.

Results

Among 317 ICERs reviewed: (A) 185 had an ICER < $50,000/QALY; (B) 53 had $50,000 ≤ ICER, < $100,000; (C) 20 had $100,000 ≤ ICER < $150,000; and (D) 59 had an ICER ≥ $150,000. Chi square testing showed a strong association (p < 0.001) between estimated ICER values and chosen CET, illustrating a lack of independence between the two. The regression analysis indicated that CETs have a baseline value of $52,000 and grow by $0.37 for each dollar increase in the estimated ICER.

Conclusions

Cost-effectiveness thresholds represent the hypothesis tests of typical CUAs. Our analysis highlights that most CUAs that cite high CETs also result in greater ICERs for the novel interventions that they investigate; thus, these interventions would otherwise not have been cost effective at lower CETs. Selection of a CET may come after the ICER is calculated to infer value that suits a hypothesis.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. 1.

    Kesselheim AS, Avorn J, Sarpatwari A. The high cost of prescription drugs in the United States: origins and prospects for reform. JAMA. 2016;316(8):858–71.

    Article  Google Scholar 

  2. 2.

    Pandya A. Adding cost-effectiveness to define low-value care. JAMA. 2018;319(19):1977–8.

    Article  Google Scholar 

  3. 3.

    Sanders GD, Neumann PJ, Basu A, Brock DW, Feeny D, Krahn M, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016;316(10):1093–103.

    Article  Google Scholar 

  4. 4.

    Neumann PJ, Kim DD, Trikalinos TA, Sculpher MJ, Salomon JA, Prosser LA, et al. Future directions for cost-effectiveness analyses in health and medicine. Med Decis Mak. 2018;38(7):767–77.

    Article  Google Scholar 

  5. 5.

    Sculpher M, Claxton K, Pearson SD. Developing a value framework: the need to reflect the opportunity costs of funding decisions. Value Health. 2017;20(2):234–9.

    Article  Google Scholar 

  6. 6.

    Woods B, Revill P, Sculpher M, Claxton K. Country-level cost-effectiveness thresholds: initial estimates and the need for further research. Value Health. 2016;19(8):929–35.

    Article  Google Scholar 

  7. 7.

    Gold M, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in health and medicine. New York: Oxford University Press; 1996.

    Google Scholar 

  8. 8.

    Neumann PJ, Sanders GD, Russel LB, Siegel JE, Ganiats TG, editors. Cost-effectiveness in health and medicine. 2nd ed. New York: Oxford University Press; 2017.

    Google Scholar 

  9. 9.

    Walker S, Griffin S, Asaria M, Tsuchiya A, Sculpher M. Striving for a societal perspective: a framework for economic evaluations when costs and effects fall on multiple sectors and decision makers. Appl Health Econ Health Policy. 2019;17(5):577–90.

    Article  Google Scholar 

  10. 10.

    Grosse SD. Assessing cost-effectiveness in healthcare: history of the $50,000 per QALY threshold. Expert Rev Pharmacoecon Outcomes Res. 2008;8(2):165–78.

    Article  Google Scholar 

  11. 11.

    Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness: the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371(9):796–7.

    CAS  Article  Google Scholar 

  12. 12.

    World Health Organization. Macroeconomics and health: investing in health for economic development. Geneva: World Health Organization; 2001.

    Google Scholar 

  13. 13.

    Anderson JL, Heidenreich PA, Barnett PG, Creager MA, Fonarow GC, Gibbons RJ, et al. ACC/AHA statement on cost/value methodology in clinical practice guidelines and performance measures: a report of the American College of Cardiology/American Heart Association Task Force on Performance Measures and Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(21):2304–22.

    Article  Google Scholar 

  14. 14.

    The World Bank. GDP per capita (current US$), 2015. 2016. http://data.worldbank.org/indicator/NY.GDP.PCAP.CD. Accessed 20 Feb 2018.

  15. 15.

    Phelps CE. A new method to determine the optimal willingness to pay in cost-effectiveness analysis. Value Health. 2019;22(7):785–91.

    Article  Google Scholar 

  16. 16.

    Snijders TAB, Bosker RJ. Multilevel analysis: an introduction to basic and advanced multilevel modeling. 2nd ed. Los Angeles (CA): Sage Publications; 2012.

    Google Scholar 

  17. 17.

    Bell CM, Urbach DR, Ray JG, Bayoumi A, Rosen AB, Greenberg D, et al. Bias in published cost effectiveness studies: systematic review. BMJ. 2006;332(7543):699–703.

    Article  Google Scholar 

  18. 18.

    Institute for Clinical and Economic Review. 2020-2023 value assessment framework. Cambdridge: Institute for Clinical and Economic Review; 2020.

    Google Scholar 

  19. 19.

    Holtgrave DR, Qualls NL, Graham JD. Economic evaluation of HIV prevention programs. Ann Rev Public Health. 1996;17:467–88.

    CAS  Article  Google Scholar 

  20. 20.

    Freedberg KA, Tosteson AN, Cotton JD, Goldman L. Optimal management strategies for HIV-infected patients who present with cough or dyspnea: a cost-effectiveness analysis. J Gen Intern Med. 1992;7:261–72.

    CAS  Article  Google Scholar 

  21. 21.

    Garber AM, Phelps CE. Economic foundations of cost-effectiveness analysis. J Health Econ. 1997;16:1–31.

    CAS  Article  Google Scholar 

  22. 22.

    Towse A, Pritchard C. National Institute for Clinical Excellence (NICE): is economic appraisal working? Pharmacoeconomics. 2002;20(Suppl. 3):95–105.

    Article  Google Scholar 

  23. 23.

    Bridges JF, Onukwugha E, Mullins CD. Healthcare rationing by proxy: cost-effectiveness analysis and the misuse of the $50,000 threshold in the US. Pharmacoeconomics. 2010;28(3):175–84.

    Article  Google Scholar 

  24. 24.

    Braithwaite RS. Willingness to pay for a quality-adjusted life year in search of a standard. Med Care. 2008;46(4):349–56.

    Article  Google Scholar 

  25. 25.

    Hirth RA. Willingness to pay for a quality-adjusted life year in search of a standard. Med Decis Mak. 2000;20(3):332–42.

    CAS  Article  Google Scholar 

  26. 26.

    Weinstein MC. How much are Americans willing to pay for a quality-adjusted life year? Med Care. 2008;46(4):343–5.

    Article  Google Scholar 

  27. 27.

    Pauly MV. The questionable economic case for value-based drug pricing in market health systems. Value Health. 2017;20(2):278–82.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Contributions

All authors contributed equally to this manuscript in terms of conceptualization, data analysis, and finalization of the manuscript in accordance with ICMJE guidelines.

Corresponding author

Correspondence to William V. Padula.

Ethics declarations

Funding

William Padula is supported by a grant from the National Institutes of Health Office of Extramural Research (KL2 TR001854).

Conflicts of interest/competing interests

William Padula is a consultant for Monument Analytics and is on the scientific advisory board for Molnlycke Healthcare. Charles Phelps is a consultant for Merck, Pfizer, and Audentes Therapeutics. Hui-Han Chen has no conflicts of interest to declare.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Availability of Data and Material

Our data are openly available in the published literature through MEDLINE. We have organized these data for public access in the Electronic Supplementary Material provided (Table e1), which aggregate data used for analysis in this study.

Code Availability

Not applicable.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 50 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Padula, W.V., Chen, HH. & Phelps, C.E. Is the Choice of Cost-Effectiveness Threshold in Cost-Utility Analysis Endogenous to the Resulting Value of Technology? A Systematic Review. Appl Health Econ Health Policy 19, 155–162 (2021). https://doi.org/10.1007/s40258-020-00606-4

Download citation