IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v7y2009i4p399-443.html
   My bibliography  Save this article

Information, learning, and drug diffusion: The case of Cox-2 inhibitors

Author

Listed:
  • Pradeep Chintagunta

    ()

  • Renna Jiang

    ()

  • Ginger Jin

    ()

Abstract

The recent withdrawal of Cox-2 Inhibitors has generated debate on the role of information in drug diffusion: can the market learn the efficacy of new drugs, or does it depend solely on manufacturer advertising and FDA updates? In this study, we use a novel data set to study the diffusion of three Cox-2 Inhibitors ? Celebrex, Vioxx and Bextra ? before the Vioxx withdrawal. Our study has two unique features: first, we observe each patient?s reported satisfaction after consuming a drug. This patient level data set, together with market level data on FDA updates, media coverage, academic articles, and pharmaceutical advertising, allows us to model individual prescription decisions. Second, we distinguish across-patient learning of a drug?s general efficacy from the within-patient learning of the match between a drug and a patient. Our results suggest that prescription choice is sensitive to many sources of information. At the beginning of 2001 and upon Bextra entry in January 2002, doctors held a strong prior belief about the efficacy of Celebrex, Vioxx, and Bextra. As a result, the learning from patient satisfaction is gradual and more concentrated on drug-patient match than on across-patient spillovers. News articles are weakly beneficial for Cox-2 drug sales, but academic articles appear to be detrimental. The impact of FDA updates is close to zero once we control for academic articles, which suggests that FDA updates follow academic articles and therefore deliver little new information to doctors. We find that drug advertising also influences the choice of a patient?s medication. A number of counterfactual experiments are carried out to quantify the influence of information on market shares.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.
  • Handle: RePEc:kap:qmktec:v:7:y:2009:i:4:p:399-443
    DOI: 10.1007/s11129-009-9072-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11129-009-9072-1
    Download Restriction: Access to full text is restricted to subscribers.

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Venkataraman, S. & Stremersch, S., 2007. "The Debate on Influencing Doctors’ Decisions: Are Drug Characteristics the Missing Link?," ERIM Report Series Research in Management ERS-2007-056-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Ching, Andrew T., 2010. "Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 619-638, November.
    3. Berndt, Ernst R, et al, 1995. "Information, Marketing, and Pricing in the U.S. Antiulcer Drug Market," American Economic Review, American Economic Association, vol. 85(2), pages 100-105, May.
    4. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    5. Sriram Venkataraman & Stefan Stremersch, 2007. "The Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?," Management Science, INFORMS, vol. 53(11), pages 1688-1701, November.
    6. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    7. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    8. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    9. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    10. Pierre Azoulay, 2002. "Do Pharmaceutical Sales Respond to Scientific Evidence?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(4), pages 551-594, December.
    11. Ackerberg, Daniel A, 2001. "Empirically Distinguishing Informative and Prestige Effects of Advertising," RAND Journal of Economics, The RAND Corporation, vol. 32(2), pages 316-333, Summer.
    12. Coscelli, Andrea & Shum, Matthew, 2004. "An empirical model of learning and patient spillovers in new drug entry," Journal of Econometrics, Elsevier, vol. 122(2), pages 213-246, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ajay Kalra & Shibo Li & Wei Zhang, 2011. "Understanding Responses to Contradictory Information About Products," Marketing Science, INFORMS, vol. 30(6), pages 1098-1114, November.
    2. repec:bla:randje:v:47:y:2016:i:4:p:1029-1056 is not listed on IDEAS
    3. Nuno Camacho & Bas Donkers & Stefan Stremersch, 2011. "Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality," Marketing Science, INFORMS, vol. 30(2), pages 305-320, 03-04.
    4. Simon P. Anderson & Federico Ciliberto & Jura Liaukonyte & Régis Renault, 2016. "Push-me pull-you: comparative advertising in the OTC analgesics industry," RAND Journal of Economics, RAND Corporation, vol. 47(4), pages 1029-1056, November.
    5. repec:eee:ijrema:v:30:y:2013:i:3:p:219-235 is not listed on IDEAS
    6. repec:eee:ijrema:v:31:y:2014:i:1:p:65-77 is not listed on IDEAS
    7. Andrew J. Epstein & Jonathan D. Ketcham, 2014. "Information technology and agency in physicians' prescribing decisions," RAND Journal of Economics, RAND Corporation, vol. 45(2), pages 422-448, June.
    8. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    9. Maurer, J. & Harris, K.M., 2015. "Learning to trust flu shots: quasi-experimental evidence on the role of learning in influenza vaccination decisions from the 2009 influenza A/H1N1 (swine flu) pandemic," Health, Econometrics and Data Group (HEDG) Working Papers 15/19, HEDG, c/o Department of Economics, University of York.

    More about this item

    Keywords

    Learning; Drug diffusion; Prescription choice; Patient satisfaction; D8; I1; M3; C5;

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L84 - Industrial Organization - - Industry Studies: Services - - - Personal, Professional, and Business Services

    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:kap:qmktec:v:7:y:2009:i:4:p:399-443. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.