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HEADROOM BEYOND THE QUALITY- ADJUSTED LIFE-YEAR: THE CASE OF COMPLEX PEDIATRIC NEUROLOGY

Part of: Special Collection - Methods

Published online by Cambridge University Press:  30 May 2017

Kirsten J.M. van Nimwegen ,
Richard J. Lilford ,
Gert J. van der Wilt  and
Janneke P.C. Grutters
Kirsten J.M. van Nimwegen
Affiliation:
Radboud University Medical Center, Department for Health Evidence, Radboud Institute for Health [email protected]
Richard J. Lilford
Affiliation:
University of Warwick, Centre for Applied Health Research and Delivery, University of Warwick
Gert J. van der Wilt
Affiliation:
Radboud University Medical Center, Department for Health Evidence, Donders Centre for Neuroscience
Janneke P.C. Grutters
Affiliation:
Radboud University Medical Center, Department for Health Evidence, Radboud Institute for Health Sciences
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Abstract

Objectives: The headroom method was introduced for the very early evaluation of the potential value of new technologies. It allows for establishing a ceiling price for technologies to still be cost-effective by combining the maximum effect a technology might yield, the maximum willingness-to-pay (WTP) for this effect, and potential downstream expenses and savings. Although the headroom method is QALY-based, not all innovations are expected to result in QALY gain.

Methods: This study explores the feasibility and usefulness of the headroom method in the evaluation of technologies that are unlikely to result in QALY gain. This will be illustrated with the diagnostic trajectory of complex pediatric neurology (CPN).

Results: Our headroom analysis showed a large room for improvement in the current diagnostic trajectory of CPN in terms of diagnostic yield. Combining this with a maximum WTP value for an additional diagnosis and the potential downstream expenses and savings, resulted in a total headroom of €15,028. This indicates that a new technology in this particular diagnostic trajectory, might be cost-effective as long as its costs do not exceed €15,028.

Conclusions: The headroom method seems a useful tool in the very early evaluation of medical technologies, also in cases when immediate QALY gain is unlikely. It allows for allocating healthcare resources to those technologies that are most promising. It should be kept in mind, however, that the headroom assumes an optimistic scenario, and for that reason cannot guarantee future cost-effectiveness. It might be most useful for ruling out those technologies that are unlikely to be cost-effective.

Keywords

Headroom analysisHealth economic evaluationDiagnostic technologiesHealth technology assessment
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 33 , Issue 1 , 2017 , pp. 5 - 10
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

1. Bodenheimer, T. High and rising health care costs. Part 2: Technologic innovation. Ann Intern Med. 2005;142:932–937.Google Scholar
2. Vallejo-Torres, L, Steuten, L, Parkinson, B, Girling, AJ, Buxton, MJ. Integrating health economics into the product development cycle: A case study of absorbable pins for treating hallux valgus. Med Decis Making. 2011;31:596–610.Google Scholar
3. Girling, A, Young, T, Brown, C, Lilford, R. Early-stage valuation of medical devices: The role of developmental uncertainty. Value Health. 2010;13:585–591.Google Scholar
4. Vallejo-Torres, L, Steuten, LM, Buxton, MJ, et al. Integrating health economics modeling in the product development cycle of medical devices: A Bayesian approach. Int J Technol Access Health Care. 2008;24:459–464.Google Scholar
5. Cosh, E, Girling, A, Lilford, R, McAteer, H, Young, T. Investing in new medical technologies: A decision framework. J Commer Biotechnol. 2007;13:263–271.Google Scholar
6. Postmus, D, de Graaf, G, Hillege, HL, Steyerberg, EW, Buskens, E. A method for the early health technology assessment of novel biomarker measurement in primary prevention programs. Stat Med. 2012;31:2733–2744.Google Scholar
7. van de Wetering, G, Steuten, LM, von Birgelen, C, Adang, EM, IJzerman, MJ. Early Bayesian modeling of a potassium lab-on-a-chip for monitoring of heart failure patients at increased risk of hyperkalaemia. Technol Forecast Soc Change. 2012;79:1268–1279.Google Scholar
8. ISPOR. International Society for Pharmacoeconomics and Outcomes Research. ISPOR Pharmacoeconomic Guidelines Around The World. 2015. http://www.ispor.org/PEguidelines/index.asp (accessed March 16, 2015).Google Scholar
9. van Nimwegen, KJM, Schieving, JH, Willemsen, MA, et al. The diagnostic pathway in complex paediatric neurology: A cost analysis. Eur J Paediatr Neurol. 2015;19:233–239.Google Scholar
10. Hoch, JS, Briggs, AH, Willan, AR. Something old, something new, something borrowed, something blue: A framework for the marriage of health econometrics and cost-effectiveness analysis. Health Econ. 2002;11:415–430.CrossRefGoogle Scholar
11. McAteer, H. The use of health economics in the early evaluation of regenerative medicine therapies. PhD thesis. 2010.Google Scholar
12. McAteer, H, Cosh, E, Freeman, G, et al. Cost‐effectiveness analysis at the development phase of a potential health technology: Examples based on tissue engineering of bladder and urethra. J Tissue Eng Regener Med. 2007;1:343–349.Google Scholar
13. NICE. Guide to the methods of technology appraisal 2013. http://publications.nice.org.uk/pmg9 (accessed April 14, 2014).Google Scholar
14. Buchanan, J, Wordsworth, S, Schuh, A. Issues surrounding the health economic evaluation of genomic technologies. Pharmacogenomics. 2013;14:1833–1847.Google Scholar
15. Regier, DA, Friedman, JM, Makela, N, Ryan, M, Marra, CA. Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: Willingness to pay from families of affected children. Clin Genet. 2009;75:514–521.Google Scholar
16. Statistics Netherlands. Consumer prices; price index 2006 = 100, 1996-2015. [cited 2016]. http://statline.cbs.nl/Statweb/publication/?DM=SLNL&PA=71311ned&D1=0-1,4-5&D2=0&D3=169-180,232,245,258-271&HDR=G1,T&STB=G2&VW=T (accessed February 7, 2017.Google Scholar
17. de Ligt, J, Willemsen, MH, van Bon, BWM, et al. Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med. 2012;367:1921–1929.Google Scholar
18. Chapman, A, Taylor, C, Girling, A, editors. Early HTA to inform medical device development decisions-the headroom method. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. New York: Springer; 2014.Google Scholar
19. Girling, A, Lilford, R, Cole, A, Young, T. Headroom approach to device development: Current and future directions. Int J Technol Assess Health Care. 2015;31:331–338.CrossRefGoogle ScholarPubMed
20. Lin, PJ, Cangelosi, MJ, Lee, DW, Neumann, PJ. Willingness to pay for diagnostic technologies: A review of the contingent valuation literature. Value Health. 2013;16:797–805.CrossRefGoogle ScholarPubMed
21. Johnson, FR, Lancsar, E, Marshall, D, 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;16:3–13.Google Scholar
22. Lancsar, E, Louviere, J. Conducting discrete choice experiments to inform healthcare decision making: A user's guide. Pharmacoeconomics. 2008;26:661–677.CrossRefGoogle ScholarPubMed
23. Grosse, SD, Wordsworth, S, Payne, K. Economic methods for valuing the outcomes of genetic testing: Beyond cost-effectiveness analysis. Genet Med. 2008;10:648–654.Google Scholar
24. Guest, G, Namey, E. Public health research methods. Thousand Oaks, CA: SAGE Publications; 2014.Google Scholar