Modeling in pharmacoeconomic studies: funding sources and outcomes

Int J Technol Assess Health Care. 2010 Jul;26(3):330-3. doi: 10.1017/S0266462310000322.


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.

Publication types

  • Review

MeSH terms

  • Conflict of Interest*
  • Costs and Cost Analysis
  • Drug Evaluation / economics*
  • Drug Industry
  • Economics, Pharmaceutical*
  • Markov Chains
  • Models, Economic*