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UNDERSTANDING AND ANTICIPATING LAG-TIME BIAS IN COST-EFFECTIVENESS STUDIES: THE ROLE OF TIME IN COST-EFFECTIVENESS ANALYSIS

Part of: Special Collection - Methods

Published online by Cambridge University Press:  30 March 2015

Gijs van de Wetering ,
Marcel Olde Rikkert ,
Gert Jan van der Wilt  and
Eddy Adang
Gijs van de Wetering
Affiliation:
Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
Marcel Olde Rikkert
Affiliation:
Department of Geriatrics, Radboud University Medical Center, Nijmegen, The Netherlands
Gert Jan van der Wilt
Affiliation:
Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlandseddy.adang@radboudumc.nl
Eddy Adang
Affiliation:
Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlandseddy.adang@radboudumc.nl
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Abstract

Background: Timely provision of information on the cost-effectiveness of innovations in health care becomes more and more important, resulting in increasing pressure on researchers to provide proof of cost-effectiveness in a short time frame. However, most of these innovations require considerable time and effort to optimally implement leading to a biased “steady state” cost-effectiveness outcome. As decision makers in health care predominantly have a short-term focus, the discrepancy between short-term study outcomes and long-term cost-effectiveness may very well lead to misguided decisions about the adoption of innovations in health care.

Methods: Factors such as learning effects, capacity constraints, and delayed time to benefit are all related to a short-run timeframe and result in inefficiencies during the implementation of an innovation. These factors and the mechanisms by which they influence the cost-effectiveness outcome are explained for three different types of healthcare innovations.

Results: As standard cost-effectiveness analysis assumes costs and effects to behave constant and representative for an innovation's entire economic lifetime, resulting cost-effectiveness outcomes might give a biased, and often overly pessimistic, reflection of the actual cost-effectiveness of an innovation. This is further amplified by the fact that short-run inefficiencies are most prevalent and impactful during an innovation's earliest stage of operation.

Conclusions: This study advocates to carefully take into account the different factors contributing to lag-time bias in the design and analysis of cost-effectiveness studies, and to communicate potential biases due to short-run inefficiencies to all stakeholders involved in the decision making process.

Keywords

Cost-effectivenessTimeInefficienciesBias
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 30 , Issue 6 , December 2014 , pp. 608 - 611
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

1. Reelick, MF, Faes, MC, Esselink, RA, Kessels, RP, Olde Rikkert, MG. How to perform a preplanned process evaluation for complex interventions in geriatric medicine: Exemplified with the process evaluation of a complex falls-prevention program for community-dwelling frail older fallers. J Am Med Dir Assoc. 2011;12:331–336.Google Scholar
2. Faes, MC, Reelick, MF, Esselink, RA, Rikkert, MG. Developing and evaluating complex healthcare interventions in geriatrics: The use of the medical research council framework exemplified on a complex fall prevention intervention. J Am Geriatr Soc. 2010;58:2212–2221.Google Scholar
3. Holmes, HM, Hayley, DC, Alexander, GC, Sachs, GA. Reconsidering medication appropriateness for patients late in life. Arch Intern Med. 2006;166:605–609.Google Scholar
4. Adang, EM, Voordijk, L, Van der Wilt, GJ, Ament, A. Cost-effectiveness analysis in relation to budgetary constraints and reallocative restrictions. Health Policy. 2005;74:146–156.Google Scholar
5. van de Wetering, G, Woertman, WH, Adang, EM. Time to incorporate time in cost-effectiveness analysis. Eur J Health Econ. 2012;13:223–226.Google Scholar
6. Argote, L, Epple, D. Learning Curves in Manufacturing. Science. 1990;247:920–924.CrossRefGoogle Scholar
7. Anzanello, MJ, Fogliatto, FS. Learning curve models and applications: Literature review and research directions. Int J Ind Ergon. 2011;41:573–583.CrossRefGoogle Scholar
8. Ramsay, CR, Grant, AM, Wallace, SA, et al. Assessment of the learning curve in health technologies. A systematic review. Int J Technol Assess Health Care. 2000;16:1095–1108.Google Scholar
9. Broeders, JA, Draaisma, WA, Rijnhart-de Jong, HG, et al. Impact of surgeon experience on 5-year outcome of laparoscopic Nissen fundoplication. Arch Surg. 2011;146:340–346.Google Scholar
10. Bais, JE, Bartelsman, JF, Bonjer, HJ, et al. Laparoscopic or conventional Nissen fundoplication for gastro-oesophageal reflux disease: Randomised clinical trial. Lancet. 2000;355:170–174.Google Scholar
11. Smet, M. Cost characteristics of hospitals. Soc Sci Med. 2002;55:895–906.Google Scholar
12. van de Wetering, G, Woertman, WH, Adang, EM. A model to correct for short run inefficiencies in health care. Health Econ. 2012;21:270–281.Google Scholar
13. van de Wetering, G, Woertman, WH, Verbeek, AL, Broeders, MJ, Adang, EM. Quantifying short run cost-effectiveness during a gradual implementation process. Eur J Health Econ. 2013;14:911–918.Google Scholar
14. Ray, KK, Cannon, CP. Time to benefit. An emerging concept for assessing the efficacy of statin therapy in cardiovascular disease. Crit Pathw Cardiol. 2005;4:43–45.Google Scholar
15. Buchwald, H, Moore, RB, Rucker, RD Jr, et al. Clinical angiographic regression of atherosclerosis after partial ileal bypass. Atherosclerosis. 1983;46:117–128.Google Scholar
16. Buchwald, H, Varco, RL, Matts, JP, et al. Effect of partial ileal bypass surgery on mortality and morbidity from coronary heart disease in patients with hypercholesterolemia. Report of the Program on the Surgical Control of the Hyperlipidemias (POSCH). N Engl J Med. 1990;323:946–955.Google Scholar
17. Chancellor, JV, Hunsche, E, de Cruz, E, Sarasin, FP. Economic evaluation of celecoxib, a new cyclo-oxygenase 2 specific inhibitor, in Switzerland. Pharmacoeconomics. 2001;19 (Suppl 1):59–75.CrossRefGoogle ScholarPubMed
18. Brown, TJ, Hooper, L, Elliott, RA, et al. A comparison of the cost-effectiveness of five strategies for the prevention of non-steroidal anti-inflammatory drug-induced gastrointestinal toxicity: A systematic review with economic modelling. Health Technol Assess. 2006;10:iii-iv, xi-xiii, 1–183.CrossRefGoogle ScholarPubMed
19. Drummond, M, Cooke, J, Walley, T. Economic evaluation under managed competition: Evidence from the UK. Soc Sci Med. 1997;45:583–95.CrossRefGoogle Scholar
20. Van Brabandt, H, Neyt, M, Hulstaert, F. Transcatheter aortic valve implantation (TAVI): Risky and costly. BMJ. 2012;345:e4710.Google Scholar