IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v30y2012i10p859-868.html
   My bibliography  Save this article

Private Manufacturers’ Thresholds to Invest in Comparative Effectiveness Trials

Author

Listed:
  • Anirban Basu
  • David Meltzer

Abstract

The recent rush of enthusiasm for public investment in comparative effectiveness research (CER) in the US has focussed attention on these public investments. However, little attention has been given to how changing public investment in CER may affect private manufacturers’ incentives for CER, which has long been a major source of CER. In this work, based on a simple revenue maximizing economic framework, we generate predictions on thresholds to invest in CER for a private manufacturer that compares its own product to a competitor’s product in head-to-head trials. Our analysis shows that private incentives to invest in CER are determined by how the results of CER may affect the price and quantity of the product sold and the duration over which resulting changes in revenue would accrue, given the time required to complete CER and the time from the completion of CER to the time of patent expiration. We highlight the result that private incentives may often be less than public incentives to invest in CER and may even be negative if the likelihood of adverse findings is sufficient. We find that these incentives imply a number of predictions about patterns of CER and how they will be affected by changes in public financing of CER and CER methods. For example, these incentives imply that incumbent patent holders may be less likely to invest in CER than entrants and that public investments in CER may crowd out similar private investments. In contrast, newer designs and methods for CER, such as Bayesian adaptive trials, which can reduce ex post risk of unfavourable results and shorten the time for the production of CER, may increase the expected benefits of CER and may tend to increase private investment in CER as long as the costs of such innovative designs are not excessive. Bayesian approaches to design also naturally highlight the dynamic aspects of CER, allowing less expensive initial studies to guide decisions about future investments and thereby encouraging greater initial investments in CER. However, whether the potential effects we highlight of public funding of CER and of Bayesian approaches to trial design actually produce changes in private investment in CER remains an empirical question. Copyright Springer International Publishing Switzerland 2012

Suggested Citation

  • Anirban Basu & David Meltzer, 2012. "Private Manufacturers’ Thresholds to Invest in Comparative Effectiveness Trials," PharmacoEconomics, Springer, vol. 30(10), pages 859-868, October.
  • Handle: RePEc:spr:pharme:v:30:y:2012:i:10:p:859-868
    DOI: 10.2165/11597730-000000000-00000
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2165/11597730-000000000-00000
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.2165/11597730-000000000-00000?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Basu, Anirban & Jena, Anupam B. & Philipson, Tomas J., 2011. "The impact of comparative effectiveness research on health and health care spending," Journal of Health Economics, Elsevier, vol. 30(4), pages 695-706, July.
    2. David O. Meltzer & Ties Hoomans & Jeannette W. Chung & Anirban Basu & Kathryn J. Aikin & Amie C. O’Donoghue & John L. Swasy & Helen W. Sullivan & David G. T. Whitehurst & Stirling Bryan & Martyn Lew, 2011. "Minimal Modeling Approaches to Value of Information Analysis for Health Research," Medical Decision Making, , vol. 31(6), pages 785-786, November.
    3. Meltzer, David, 2001. "Addressing uncertainty in medical cost-effectiveness analysis: Implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to s," Journal of Health Economics, Elsevier, vol. 20(1), pages 109-129, January.
    4. Daron Acemoglu & Joshua Linn, 2004. "Market Size in Innovation: Theory and Evidence from the Pharmaceutical Industry," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 1049-1090.
    5. David O. Meltzer & Ties Hoomans & Jeanette W. Chung & Anirban Basu, 2011. "Minimal Modeling Approaches to Value of Information Analysis for Health Research," Medical Decision Making, , vol. 31(6), pages 1-22, November.
    6. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    7. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    8. Garber Alan M & Jones Charles I. & Romer Paul, 2006. "Insurance and Incentives for Medical Innovation," Forum for Health Economics & Policy, De Gruyter, vol. 9(2), pages 1-27, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    2. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225, November.
    3. Brennan, Alan & Kharroubi, Samer A., 2007. "Efficient computation of partial expected value of sample information using Bayesian approximation," Journal of Health Economics, Elsevier, vol. 26(1), pages 122-148, January.
    4. Darius Lakdawalla & Neeraj Sood, 2007. "The Welfare Effects of Public Drug Insurance," NBER Working Papers 13501, National Bureau of Economic Research, Inc.
    5. Böhm, Sebastian & Grossmann, Volker & Strulik, Holger, 2021. "R&D-driven medical progress, health care costs, and the future of human longevity," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    6. Patricia M. Danzon & Eric L. Keuffel, 2014. "Regulation of the Pharmaceutical-Biotechnology Industry," NBER Chapters, in: Economic Regulation and Its Reform: What Have We Learned?, pages 407-484, National Bureau of Economic Research, Inc.
    7. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    8. Charles F. Manski, 2022. "Patient‐centered appraisal of race‐free clinical risk assessment," Health Economics, John Wiley & Sons, Ltd., vol. 31(10), pages 2109-2114, October.
    9. Anna Heath & Petros Pechlivanoglou, 2022. "Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity," Medical Decision Making, , vol. 42(5), pages 649-660, July.
    10. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.
    11. Blythe Adamson & Dobromir Dimitrov & Beth Devine & Ruanne Barnabas, 2017. "The Potential Cost-Effectiveness of HIV Vaccines: A Systematic Review," PharmacoEconomics - Open, Springer, vol. 1(1), pages 1-12, March.
    12. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    13. Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
    14. Leila Agha & Soomi Kim & Danielle Li, 2020. "Insurance Design and Pharmaceutical Innovation," NBER Working Papers 27563, National Bureau of Economic Research, Inc.
    15. Haitham Tuffaha & Claire Rothery & Natalia Kunst & Chris Jackson & Mark Strong & Stephen Birch, 2021. "A Review of Web-Based Tools for Value-of-Information Analysis," Applied Health Economics and Health Policy, Springer, vol. 19(5), pages 645-651, September.
    16. Boone, Jan, 2013. "Does the market choose optimal health insurance coverage?," CEPR Discussion Papers 9420, C.E.P.R. Discussion Papers.
    17. Andrija S Grustam & Nasuh Buyukkaramikli & Ron Koymans & Hubertus J M Vrijhoef & Johan L Severens, 2019. "Value of information analysis in telehealth for chronic heart failure management," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
    18. Moshe Levy & Adi Rizansky, 2014. "Market failure in the pharmaceutical industry and how it can be overcome: the CureShare mechanism," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(2), pages 143-156, March.
    19. Boone, J., 2013. "Does the Market Choose Optimal Health Insurance Coverage," Other publications TiSEM f7691fbf-f770-4714-b1b4-1, Tilburg University, School of Economics and Management.
    20. Fleurence, Rachael L. & Torgerson, David J., 2004. "Setting priorities for research," Health Policy, Elsevier, vol. 69(1), pages 1-10, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pharme:v:30:y:2012:i:10:p:859-868. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.