IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/8621.html
   My bibliography  Save this paper

An Empirical Model of Learning under Ambiguity: The Case of Clinical Trials

Author

Listed:
  • Fernandez, Jose

Abstract

In this paper, I present an empirical model of learning under ambiguity in the context of clinical trials. Patients are concern with learning the treatment effect of the experimental drug, but face the ambiguity of random group assignment. A two dimensional Bayesian model of learning is proposed to capture patients�beliefs on the treatment effect and group assignment. These beliefs are then used to predict patient attrition in clinical trials. Patient learning is demonstrated to be slower when taking into account group ambiguity. In addition, the model corrects for attrition bias in the estimated treatment effect.

Suggested Citation

  • Fernandez, Jose, 2008. "An Empirical Model of Learning under Ambiguity: The Case of Clinical Trials," MPRA Paper 8621, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:8621
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/8621/1/MPRA_paper_8621.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sanford J. Grossman & Richard E. Kihlstrom & Leonard J. Mirman, 1977. "A Bayesian Approach to the Production of Information and Learning By Doing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 44(3), pages 533-547.
    2. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    3. Tat Y. Chan & Barton H. Hamilton, 2006. "Learning, Private Information, and the Economic Evaluation of Randomized Experiments," Journal of Political Economy, University of Chicago Press, vol. 114(6), pages 997-1040, December.
    4. Stern, Steven, 1994. "Two Dynamic Discrete Choice Estimation Problems and Simulation Method Solutions," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 695-702, November.
    5. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    6. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    7. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(2), pages 651-694.
    8. Levitt, Steven D. & List, John A., 2009. "Field experiments in economics: The past, the present, and the future," European Economic Review, Elsevier, vol. 53(1), pages 1-18, January.
    9. Malani, Anup, 2008. "Patient enrollment in medical trials: Selection bias in a randomized experiment," Journal of Econometrics, Elsevier, vol. 144(2), pages 341-351, June.
    10. 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.
    11. 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.
    12. Berkovec, James & Stern, Steven, 1991. "Job Exit Behavior of Older Men," Econometrica, Econometric Society, vol. 59(1), pages 189-210, January.
    13. Steven T. Yen, 2005. "A Multivariate Sample-Selection Model: Estimating Cigarette and Alcohol Demands with Zero Observations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 453-466.
    14. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
    15. 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.
    16. repec:feb:artefa:0087 is not listed on IDEAS
    17. Hector Chade & Gustavo Ventura, 1998. "Taxes and Marriage: A Two-Sided Search Analysis," University of Western Ontario, Departmental Research Report Series 9819, University of Western Ontario, Department of Economics.
    18. Anup Malani, 2006. "Identifying Placebo Effects with Data from Clinical Trials," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 236-256, April.
    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. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    2. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    3. Barton H. Hamilton & Andrés Hincapié & Robert A. Miller & Nicholas W. Papageorge, 2021. "Innovation And Diffusion Of Medical Treatment," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(3), pages 953-1009, August.
    4. Mark Egan & Tomas Philipson, 2016. "Health Care Adherence and Personalized Medicine," Working Papers 2016-H01, Becker Friedman Institute for Research In Economics.
    5. Jürgen Maurer & Katherine M. Harris, 2016. "Learning to Trust Flu Shots: Quasi‐Experimental Evidence from the 2009 Swine Flu Pandemic," Health Economics, John Wiley & Sons, Ltd., vol. 25(9), pages 1148-1162, September.
    6. 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.

    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. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    2. Guofang Huang & Matthew Shum & Wei Tan, 2019. "Is pharmaceutical detailing informative? Evidence from contraindicated drug prescriptions," Quantitative Marketing and Economics (QME), Springer, vol. 17(2), pages 135-160, June.
    3. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    4. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    5. Saxell, Tanja, 2014. "Industrial organization studies on pharmaceutical markets," Research Reports 65, VATT Institute for Economic Research.
    6. Saxell, Tanja, 2014. "Industrial organization studies on pharmaceutical markets," Research Reports P65, VATT Institute for Economic Research.
    7. Tat Y. Chan & Barton H. Hamilton, 2006. "Learning, Private Information, and the Economic Evaluation of Randomized Experiments," Journal of Political Economy, University of Chicago Press, vol. 114(6), pages 997-1040, December.
    8. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    9. Hu, Yingyao & Kayaba, Yutaka & Shum, Matthew, 2013. "Nonparametric learning rules from bandit experiments: The eyes have it!," Games and Economic Behavior, Elsevier, vol. 81(C), pages 215-231.
    10. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    11. Guofang Huang & Hong Luo & Jing Xia, 2019. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning," Management Science, INFORMS, vol. 65(12), pages 5556-5583, December.
    12. 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.
    13. Hai Che & Tülin Erdem & T. Sabri Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    14. Jinyang Zheng & Fei Ren & Yong Tan & Xi Chen, 2020. "Optimizing Two-Sided Promotion for Transportation Network Companies: A Structural Model with Conditional Bayesian Learning," Information Systems Research, INFORMS, vol. 31(3), pages 692-714, September.
    15. S. Sriram & Pradeep K. Chintagunta & Puneet Manchanda, 2015. "Service Quality Variability and Termination Behavior," Management Science, INFORMS, vol. 61(11), pages 2739-2759, November.
    16. Dirk Bergemann & Juuso Välimäki, 2006. "Dynamic Pricing of New Experience Goods," Journal of Political Economy, University of Chicago Press, vol. 114(4), pages 713-743, August.
    17. Hai Che & Tülin Erdem & T. Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    18. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.
    19. Michael P. Keane, 2013. "Panel data discrete choice models of consumer demand," Economics Papers 2013-W08, Economics Group, Nuffield College, University of Oxford.
    20. Sylvain Chassang & Erik Snowberg & Ben Seymour & Cayley Bowles, 2015. "Accounting for Behavior in Treatment Effects: New Applications for Blind Trials," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.

    More about this item

    Keywords

    clinical trials; learning; Bayesian; structural model; treatment effect;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I1 - Health, Education, and Welfare - - Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:8621. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.