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Early technology assessment of new medical devices

Published online by Cambridge University Press:  24 January 2008

Jan B. Pietzsch  and
M. Elisabeth Paté-Cornell
Jan B. Pietzsch
Affiliation:
Stanford University and Wing Tech Inc.
M. Elisabeth Paté-Cornell
Affiliation:
Stanford University
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Abstract

Objectives: In the United States, medical devices represent an eighty-billion dollar a year market. The U.S. Food and Drug Administration rejects a significant number of applications of devices that reach the investigational stage. The prospects of improving patient condition, as well as firms' profits, are thus substantial, but fraught with uncertainties at the time when investments and design decisions are made. This study presents a quantitative model focused on the risk aspects of early technology assessment, designed to support the decisions of medical device firms in the investment and development stages.

Methods: The model is based on the engineering risk analysis method involving systems analysis and probability. It assumes use of all evidence available (both direct and indirect) and integrates the information through a linear formula of aggregation of probability distributions. The model is illustrated by a schematic version of the case of the AtrialShaper, a device for the reduction of stroke risk that is currently in the preprototype stage.

Results: The results of the modeling provide a more complete description of the evidence base available to support early-stage decisions, thus allowing comparison of alternative designs and management alternatives.

Conclusions: The model presented here provides early-stage decision-support to industry, but also benefits regulators and payers in their later assessment of new devices and associated procedures.

Keywords

Medical deviceTechnology assessmentDecision-supportProbabilityRiskPerformanceCommercialization
Type
GENERAL ESSAYS
Information
International Journal of Technology Assessment in Health Care , Volume 24 , Issue 1 , January 2008 , pp. 36 - 44
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

1. Barnett, HJM, Eliasziw, M, Meldrum, HE. Drugs and surgery in the prevention of ischemic stroke. N Engl J Med. 1995;332:238–248.CrossRefGoogle ScholarPubMed
2. Benjamin, JR, Cornell, CA. Probability, statistics, and decision for civil engineers. New York: McGraw-Hill; 1970.Google Scholar
3. Benjamin, EJ, Wolf, PA, D'Agostino, RB, et al. . Impact of atrial fibrillation on the risk of death—The Framingham Heart Study. Circulation. 1998;98:946–952.CrossRefGoogle ScholarPubMed
4. Berry, DA. Using a Bayesian approach in medical device development. Publication for the U.S. Federal Food and Drug Administration's Center for Devices and Radiological Health, Institute of Statistics & Decision Sciences and Comprehensive Cancer Center. Durham, NC: Duke University; 1997.Google Scholar
5. Buxton, M. Early assessment of health technologies - The state of the science. Presentation at CCOHTA/Euroscan Symposium ‘Early Assessment of Health Technologies’. Ottawa, Canada: October 12, 2000.Google Scholar
6. CCOHTA. CCOHTA/Euroscan Symposium on ‘Early Assessment of Health Technologies’. Conference Summary. Ottawa, Canada: Canadian Coordinating Office for Health Technology Assessment; 2000.Google Scholar
7. Clemen, RT. Combining forecasts: A review and annotated bibliography. Int J Forecast. 1989;5:559–583.CrossRefGoogle Scholar
8. Clemen, RT, Winkler, RL. Combining probability distributions from experts in risk analysis. Risk Anal. 1999;19:187–203.CrossRefGoogle Scholar
9. Clemens, PL. Making component failure probability estimates. Tutorial Paper; Jacobs Sverdrup; 2002.Google Scholar
10. Dmochowski, RR, Galen, D. Evaluation of subacute tissue response in an animal model to evaluate a novel therapy to treat stress urinary incontinence. Presentation at the International Continence Society 30th annual meeting. Tampere, Finland; 2000.Google Scholar
11. Eddy, DM. Clinical decision making—From theory to practice. London UK: Jones and Bartlett Publishers International; 1996.Google Scholar
12. Goodman, C. HTA: Providing a scientific basis for the commercialization of innovative, cost-effective technologies. Panel presentation; ISTAHC 2003. Canmore, Canada; June 24, 2003.Google Scholar
13. Hagen, A, Dintsios, CM, Perleth, M, et al. Methods for the assessment of premarket (pm) biomedical innovations: A systematic review. In: The challenge of collaboration. Proceedings of the 18th Annual Meeting of the International Society of Technology Assessment in Health Care, Berlin; June 2002.Google Scholar
14. Henley, EJ, Kumamoto, H. Probabilistic risk assessment. New York: IEEE Press; 1991.Google Scholar
15. Howard, RA, Matheson, JE. Influence diagrams. In: Howard, RA, Matheson, JE, eds. The principles and applications of decision analysis. Menlo Park CA: Strategic Decision Group; 1984.Google Scholar
16. Laupacis, A, Boysen, G, Connolly, S, et al. Risk factors for stroke and efficacy of antithrombotic therapy in atrial fibrillation. Arch Intern Med. 1994;154:1449–1457.Google Scholar
17. Murphy, DM, Paté-Cornell, ME. The SAM framework: Modeling the effects of management factors on human behavior in risk analysis. Risk Anal. 1996;16:501–515.CrossRefGoogle Scholar
18. Murray, JW. Health technology assessment: Providing a scientific basis for the commercialization of innovative, cost-effective technologies. Panel presentation; 19th Annual Meeting of the International Society of Technology Assessment in Health Care. Canmore, Canada; June 24, 2003.Google Scholar
19. Obrzut, SL, Hecht, P, Hayashi, K, Fanton, GS. The effect of radiofrequency energy on the length and temperature properties of the glenohumeral joint capsule. Arthroscopy. 1998;14:395–400.CrossRefGoogle ScholarPubMed
20. Pietzsch, JB, Paté-Cornell, ME, Krummel, TM. A framework for probabilistic assessment of new medical technologies. In: Spitzer, C, Schmocker, U, Dang, VN, eds. Probabilistic safety assessment and management (PSAM7-ESREL'04). Proceedings of the 7th International Conference on Probabilistic Safety Assessment and Management. London UK: Springer-Verlag; 2004:2224–2229.CrossRefGoogle Scholar
21. Pietzsch, JB. Early-stage technology assessment: Methods and opportunities. Ital J Public Health 2005;2 (Suppl 1):74–75.Google Scholar
22. Powell, NB, Riley, RW, Troell, RJ, et al. Radiofrequency volumetric reduction of the tongue: A porcine pilot study for the treatment of obstructive sleep apnea syndrome. Chest. 1997;111:1348–1355.CrossRefGoogle Scholar
23. Powell, NB, Riley, RW, Troell, RJ, et al. Radiofrequency volumetric tissue reduction of the palate in subjects with sleep-disordered breathing. Chest. 1998;113:1163–1174.CrossRefGoogle ScholarPubMed
24. Szczepura, A, Kankaanpää, J. An introduction to health technology assessment. In: Szczepura, A, Kankaanpää, J, eds. Assessment of health care technologies: Case studies, key concepts, and strategic issues. New York: John Wiley & Sons; 1996.Google Scholar
25. U.S. Nuclear Regulatory Commission. Reactor safety study: An assessment of accident risks in U.S. commercial nuclear power plants. US Nuclear Regulatory Commission, NUREG-75/014; 1975.Google Scholar
26. U.S. Food and Drug Administration. Guidance for the use of Bayesian statistics in medical device clinical trials. Guidance Document; Washington DC: FDA CDRH; May 2006.Google Scholar
27. von Neumann, J, Morgenstern, O. Theory of games and economic behavior. 3rd ed. Princeton, NJ: Princeton University Press; 1953.Google Scholar
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