, Volume 34, Issue 4, pp 393–402 | Cite as

What Can We Expect from Value-Based Funding of Medicines? A Retrospective Study

Original Research Article



Deciding on public funding for pharmaceuticals on the basis of value for money is now widespread. We suggest that evidence-based assessment of value has restricted the availability of medicines in Australia in a way that reflects the relative bargaining power of government and the pharmaceutical industry. We propose a simple informal game-theoretic model of bargaining between the funding agency and industry and test its predictions using a logistic multiple regression model of past funding decisions made by the Pharmaceutical Benefits Advisory Committee in Australia.


The model estimates the probability of a drug being recommended for subsidy as a function of incremental cost per quality-adjusted life-year (QALY), as well as other drug and market characteristics. Data are major submissions or resubmissions from 1993 to 2009 where there was a claim of superiority and evidence of a difference in quality of life. Independent variables measure the incremental cost per QALY, the cost to the public budget, the strength and quality of the clinical and economic evidence, need as measured by severity of illness and the availability of alternative treatments, whether or not a resubmission, and newspaper reports as a measure of public pressure. We report the odds ratio for each variable and calculate the ratio of the marginal effect of each variable to the marginal effect of the cost per QALY as a measure of the revealed willingness to pay for each of the variables that influence the decision.


The results are consistent with a bargaining model where a 10,000 Australian dollar ($A) fall in value (increase in cost per QALY) reduces the average probability of public funding from 37 to 33 % (95 % CI 34–32). If the condition is life threatening or the drug has no active comparator, then the odds of a positive recommendation are 3.18 (95 % CI 1.00–10.11) and 2.14 (95 % CI 0.95–4.83) greater, equivalent to a $A33,000 and a $A21,000 increase in value (fall in cost per QALY). If both conditions are met, the odds are increased by 4.41 (95 % CI 1.28–15.24) times, equivalent to an increase in value of $A46,000. Funding is more likely as time elapses and price falls, but we did not find clear evidence that public or corporate pressure influences decisions.


Evidence from Australia suggests that the determinants of public funding and pricing decisions for medicines reflect the relative bargaining power of government and drug companies. Value for money depends on the quality of evidence, timing, patient need, perceived benefit and opportunity cost; these factors reflect the potential gains from striking a bargain and the risk of loss from not doing so.


  1. 1.
    Drummond M, Jönsson B, Rutten F. The role of economic evaluation in the pricing and reimbursement of medicines. Health Policy. 1997;40(3):199–215.CrossRefPubMedGoogle Scholar
  2. 2.
    Garattini L, Cornago D, De Compadri P. Pricing and reimbursement of in-patent drugs in seven European countries: a comparative analysis. Health Policy. 2007;82(3):330–9.CrossRefPubMedGoogle Scholar
  3. 3.
    Paris V, Belloni A. Value in pharmaceutical pricing. OECD Health Working Papers no. 63. Paris: OECD Publishing; 2013.Google Scholar
  4. 4.
    Eckermann S, Pekarsky B. Can the real opportunity cost stand up: displaced services, the straw man outside the room. Pharmacoeconomics. 2014;32(4):319–25.CrossRefPubMedGoogle Scholar
  5. 5.
    Nash J. Two-person cooperative games. Econometrica. 1953;21(1):128–40.CrossRefGoogle Scholar
  6. 6.
    Roth AE. Risk aversion and the relationship between Nash’s solution and subgame perfect equilibrium of sequential bargaining. J Risk Uncertainty. 1989;2(4):353–65.CrossRefGoogle Scholar
  7. 7.
    Rubinstein A. Perfect equilibrium in a bargaining model. Econometrica. 1982;50(1):97–109.CrossRefGoogle Scholar
  8. 8.
    National Institute for Health and Clinical Excellence. Social value judgements: principles for the development of NICE guidance. 2nd ed. London: NICE; 2008.Google Scholar
  9. 9.
    Australian Government Department of Health. Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee. Canberra: Australian Government Department of Health. Accessed Feb 2015.
  10. 10.
    Tappenden P, Brazier J, Ratcliffe J, Chilcott J. A stated preference binary choice experiment to explore NICE decision making. Pharmacoeconomics. 2007;25(8):685–93.CrossRefPubMedGoogle Scholar
  11. 11.
    Koopmanschap MA, Stolk EA, Koolman X. Dear policy maker: have you made up your mind? A discrete choice experiment among policy makers and other health professionals. Int J Technol Assess Health Care. 2010;26(02):198–204.CrossRefPubMedGoogle Scholar
  12. 12.
    George B, Harris A, Mitchell A. Cost-effectiveness analysis and the consistency of decision making: evidence from pharmaceutical reimbursement in australia (1991 to 1996). Pharmacoeconomics. 2001;19(11):1103–9.CrossRefPubMedGoogle Scholar
  13. 13.
    Devlin N, Parkin D. Does NICE have a cost-effectiveness threshold and what other factors influence its decisions? A binary choice analysis. Health Econ. 2004;13(5):437–52.CrossRefPubMedGoogle Scholar
  14. 14.
    Harris AH, Hill SR, Chin G, Li JJ, Walkom E. The role of value for money in public insurance coverage decisions for drugs in Australia: a retrospective analysis 1994–2004. Med Decis Making. 2008;28(5):713–22.CrossRefPubMedGoogle Scholar
  15. 15.
    Australian Government. Pharmaceutical Benefits Scheme public summary documents. Canberra: Australian Government. Accessed 21 Nov 2014.
  16. 16.
    Longo DL, Fauci AS, Kasper DL, Hauser SL, Jameson L, Loscalzo J, editors. Harrison’s principles of internal medicine. 18th ed. New York: McGraw-Hill Companies Inc.; 2012.Google Scholar
  17. 17.
    Dakin H, Devlin N, Feng Y, Rice N, O’Neill P, Parkin D. The influence of cost effectiveness and other factors on NICE decisons. Health Econ. 2014;. doi:10.1002/hec.3086.PubMedGoogle Scholar
  18. 18.
    World Health Organisation. Global burden of disease 2004 update: disability weights for diseases and conditions. Accessed 5 Dec 2014.
  19. 19.
    Productivity Commission. International pharmaceutical price differences. Canberra: AusInfo; 2001.Google Scholar
  20. 20.
    OECD. Pharmaceutical pricing policies in a global market. Paris: OECD Publishing; 2008.Google Scholar
  21. 21.
    Clarke P. Challenges and opportunities for the Pharmaceutical Benefits Scheme [editorial]. Med J Aust. 2012;196:153–4.CrossRefPubMedGoogle Scholar
  22. 22.
    Drummond M. Twenty years of using economic evaluations for drug reimbursement decisions: what has been achieved? J Health Polit Policy Law. 2013;38(6):1081–102.CrossRefPubMedGoogle Scholar
  23. 23.
    Svensson M, Nilsson FL, Arnberg K. Reimbursement decisions for pharmaceuticals in Sweden: the impact of disease severity and cost effectiveness. Pharmacoeconomics. 2015;33(11):1229–36.CrossRefPubMedGoogle Scholar
  24. 24.
    OECD Health Data 2013. Accessed 10 Nov 2013.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Centre for Health EconomicsClaytonAustralia

Personalised recommendations