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Modeling in pharmacoeconomic studies: Funding sources and outcomes

Published online by Cambridge University Press:  29 June 2010

Livio Garattini ,
Daniela Koleva  and
Gianluigi Casadei
Livio Garattini
Affiliation:
CESAV, Centre of Health Economics, Italy
Daniela Koleva
Affiliation:
CESAV, Centre of Health Economics, Italy
Gianluigi Casadei
Affiliation:
CESAV, Centre of Health Economics, Italy
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Abstract

Objectives: The prime objective of this study was to investigate whether sponsorship by the pharmaceutical industry affected the results of full economic evaluations (FEE) based on modeling. In particular, we focused on the flourishing literature based on Markov models, by far the most widely exploited tool for estimating lifetime costs and benefits.

Methods: We made a literature search of the international database PubMed to find all the studies on pharmacological treatments based on Markov models published in English in the period January 1, 2004 to June 30, 2009. We selected the FEEs focused on single drugs only, specifically cost-effectiveness and cost-utility analyses. Two hundred articles including FEEs based on Markov models were considered eligible. For the analysis, we classified the FEEs into two groups according to whether or not they had financial backing from the pharmaceutical industry. We then assessed the main conclusions, which were classified as (i) “favorable,” (ii) “doubtful,” and (iii) “unfavorable.”

Results: Of the 200 articles, 138 (69 percent) were sponsored and 162 (81 percent) reached favorable conclusions. Sponsored studies were much more likely to report favorable conclusions than nonsponsored ones (95 percent and 50 percent, p < .001), the former even omitting unfavorable conclusions.

Conclusions: The review found a substantial share of studies supported by the pharmaceutical industry, almost all concluding in favor of the drug studied, without any unfavorable conclusions at all. These results confirm also in the field of pharmacoeconomic studies that the best way of limiting confounding factors is by clearly distinguishing assessors from manufacturers and marketers of any new technology.

Keywords

Markov chainDrug therapyCosts and cost analysis
Type
METHODS
Information
International Journal of Technology Assessment in Health Care , Volume 26 , Issue 3 , July 2010 , pp. 330 - 333
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

1. Bala, MV, Mauskopf, JA. Optimal assignment of treatments to health states using a Markov decision model. An introduction to basic concepts. Pharmacoeconomics. 2006;24:345–354.CrossRefGoogle Scholar
2. Banta, HD, Andreasen, PB. The political dimension in health care technology assessments programs. Int J Technol Assess Health Care. 1990;6:115–123.CrossRefGoogle ScholarPubMed
3. Bell, CM, Urbach, DR, Ray, JG, et al. Bias in published cost effectiveness studies: Systematic review. BMJ. 2006;332:699–703.CrossRefGoogle ScholarPubMed
4. Burls, A, Sandercock, J. How to make a compelling submission to NICE: Tips for sponsoring organizations. BMJ. 2003;327:1446–1448.CrossRefGoogle Scholar
5. Conway, PH, Clancy, C. Comparative-effectiveness research— Implications of the Federal Coordinating Council's Report. N Engl J Med. 2009;361:328–330.CrossRefGoogle ScholarPubMed
6. Dong, FB, Sorensen, SW, Manninen, DL, et al. Cost effectiveness of ACE inhibitor treatment for patients with type 1 diabetes mellitus. Pharmacoeconomics. 2004;22:1015–1027.CrossRefGoogle ScholarPubMed
7. Garattini, L, Casadei, G. Health technology assessment: For whom the bell tolls? Eur J Health Econ. 2008;9:311–312.CrossRefGoogle ScholarPubMed
8. Gerkens, S, Nechelput, M, Annemans, L, et al. A health economic model to assess the cost-effectiveness of PEG IFN α-2a and ribavirin in patients with mild chronic hepatitis C. J Viral Hepat. 2007;14:523–536.CrossRefGoogle ScholarPubMed
9. Iglehart, JK. Priorizing Comparative-effectiveness research – IOM recommendations. N Engl J Med. 2009;361:325–327.CrossRefGoogle Scholar
10. Krimsky, S. Conflict of Interest and Cost-effectiveness Analysis. JAMA. 1999;282:1474–1475.CrossRefGoogle ScholarPubMed
11. Lexchin, J, Bero, LA, Djulbegovic, B, Clark, O. Pharmaceutical industry sponsorship and research outcome and quality: Systematic review. BMJ. 2003;326:1167–1170.CrossRefGoogle ScholarPubMed
12. OECD. Health update. No. 7. July 2009. http://www.oecd.org/health/update (accessed July 16, 2009).Google Scholar
13. Philips, Z, Ginnelly, L, Sculpher, M, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess. 2004;8:1–158.CrossRefGoogle ScholarPubMed
14. Steinbrook, R. Controlling conflict of interest – Proposal from the Institute of Medicine. N Engl J Med. 2009;360:2160–2163.CrossRefGoogle ScholarPubMed
15. Volpp, KG, Das, A. Comparative effectiveness – Thinking beyond medication A versus medication B. N Engl J Med. 2009;361:331–333.CrossRefGoogle ScholarPubMed
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