IDEAS home Printed from https://ideas.repec.org/a/eee/jetheo/v184y2019ics0022053119300808.html
   My bibliography  Save this article

Controls, belief updating, and bias in medical RCTs

Author

Listed:
  • Chemla, Gilles
  • Hennessy, Christopher A.

Abstract

We develop a formal model of placebo effects. If subjects in seemingly-ideal single-stage RCTs update beliefs about breakthroughs based upon personal physiological responses, mental effects differ across medications received, treatment versus control. Consequently, the average cross-arm health difference becomes a biased estimator. Constructively, we show: bias can be altered through choice of control; higher-efficacy controls mitigate upward bias; and efficacy states can be revealed through controls of intermediate efficacy or controls that mimic a subset of efficacy states. Consistent with experimental evidence, our theory implies outcomes within-arm and cross-arm differences can be non-monotone in treatment probability. Finally, we develop novel differences-in-differences and triangle equality tests to detect RCT bias.

Suggested Citation

  • Chemla, Gilles & Hennessy, Christopher A., 2019. "Controls, belief updating, and bias in medical RCTs," Journal of Economic Theory, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:jetheo:v:184:y:2019:i:c:s0022053119300808
    DOI: 10.1016/j.jet.2019.07.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022053119300808
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jet.2019.07.016?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. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    2. 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.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    4. 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.
    5. Sylvain Chassang & Gerard Padro I Miquel & Erik Snowberg, 2012. "Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments," American Economic Review, American Economic Association, vol. 102(4), pages 1279-1309, June.
    6. , G. & , & ,, 2008. "Non-Bayesian updating: A theoretical framework," Theoretical Economics, Econometric Society, vol. 3(2), June.
    7. 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.
    8. Larry G. Epstein, 2006. "An Axiomatic Model of Non-Bayesian Updating," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(2), pages 413-436.
    9. Andrew Caplin & John Leahy, 2001. "Psychological Expected Utility Theory and Anticipatory Feelings," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 55-79.
    10. 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. Hennessy, Christopher A. & Chemla, Gilles, 2022. "Signaling, instrumentation, and CFO decision-making," Journal of Financial Economics, Elsevier, vol. 144(3), pages 849-863.
    2. Chemla, Gilles & Hennessy, Christopher, 2020. "Signaling, Random Assignment, and Causal Effect Estimation," CEPR Discussion Papers 15175, C.E.P.R. Discussion Papers.
    3. Custódio, Cláudia & Mendes, Diogo & Metzger, Daniel, 2020. "The Impact of Financial Education of Managers on Medium and Large Enterprises - A Randomized Controlled Trial in Mozambique," CEPR Discussion Papers 15294, C.E.P.R. Discussion Papers.
    4. Claudia Custodio & Diogo Mendes & Daniel Metzger, 2021. "The impact of financial education of executives on financial practices of medium and large enterprises," NOVAFRICA Working Paper Series wp2105, Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA.

    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. Sylvain Chassang & Gerard Padro I Miquel & Erik Snowberg, 2012. "Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments," American Economic Review, American Economic Association, vol. 102(4), pages 1279-1309, June.
    2. Chemla, Gilles & Hennessy, Christopher, 2016. "Subject Rational Expectations Will Contaminate Randomized Controlled Medical Trials," CEPR Discussion Papers 11360, C.E.P.R. Discussion Papers.
    3. 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.
    4. Daniele Pennesi, 2020. "Identity and information acquisition," Carlo Alberto Notebooks 610, Collegio Carlo Alberto, revised 2021.
    5. Gani Aldashev & Georg Kirchsteiger & Alexander Sebald, 2017. "Assignment Procedure Biases in Randomised Policy Experiments," Economic Journal, Royal Economic Society, vol. 127(602), pages 873-895, June.
    6. Anup Malani & Tomas J. Philipson, 2011. "Can Medical Progress be Sustained? Implications of the Link Between Development and Output Markets," NBER Working Papers 17011, National Bureau of Economic Research, Inc.
    7. Jose M. Fernandez, 2013. "An Empirical Model Of Learning Under Ambiguity: The Case Of Clinical Trials," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(2), pages 549-573, May.
    8. Chemla, Gilles & Hennessy, Christopher A., 2020. "Rational expectations and the Paradox of policy-relevant natural experiments," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 368-381.
    9. Abhijit Banerjee & Sylvain Chassang & Erik Snowberg, 2016. "Decision Theoretic Approaches to Experiment Design and External Validity," NBER Working Papers 22167, National Bureau of Economic Research, Inc.
    10. André Lapied & Thomas Rongiconi, 2013. "Ambiguity as a Source of Temptation: Modeling Unstable Beliefs," Working Papers halshs-00797631, HAL.
    11. Riella, Gil, 2013. "Preference for Flexibility and Dynamic Consistency," Journal of Economic Theory, Elsevier, vol. 148(6), pages 2467-2482.
    12. Yusuke Narita, 2018. "Toward an Ethical Experiment," Cowles Foundation Discussion Papers 2127, Cowles Foundation for Research in Economics, Yale University.
    13. Parente, Paulo M.D.C. & Santos Silva, J.M.C., 2012. "A cautionary note on tests of overidentifying restrictions," Economics Letters, Elsevier, vol. 115(2), pages 314-317.
    14. Abhijit Banerjee & Rukmini Banerji & James Berry & Esther Duflo & Harini Kannan & Shobhini Mukerji & Marc Shotland & Michael Walton, 2017. "From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application," Journal of Economic Perspectives, American Economic Association, vol. 31(4), pages 73-102, Fall.
    15. Zhang, Hanzhe, 2013. "Evolutionary justifications for non-Bayesian beliefs," Economics Letters, Elsevier, vol. 121(2), pages 198-201.
    16. Breen, Richard & Ermisch, John, 2021. "Instrumental Variable Estimation in Demographic Studies: The LATE interpretation of the IV estimator with heterogenous effects," SocArXiv vx9m7, Center for Open Science.
    17. Cristina Corduneanu-Huci & Michael T. Dorsch & Paul Maarek, 2017. "Learning to constrain: Political competition and randomized controlled trials in development," THEMA Working Papers 2017-24, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    18. Jörg Peters & Jörg Langbein & Gareth Roberts, 2018. "Generalization in the Tropics – Development Policy, Randomized Controlled Trials, and External Validity," The World Bank Research Observer, World Bank, vol. 33(1), pages 34-64.
    19. Mariella Gonzales & Gianmarco León-Ciliotta & Luis R. Martínez, 2022. "How Effective Are Monetary Incentives to Vote? Evidence from a Nationwide Policy," American Economic Journal: Applied Economics, American Economic Association, vol. 14(1), pages 293-326, January.
    20. Yusuke Narita, 2018. "Experiment-as-Market: Incorporating Welfare into Randomized Controlled Trials," Cowles Foundation Discussion Papers 2127r, Cowles Foundation for Research in Economics, Yale University, revised May 2019.

    More about this item

    Keywords

    Belief updating; Medical RCTs; Bias; Control; Treatment;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • K32 - Law and Economics - - Other Substantive Areas of Law - - - Energy, Environmental, Health, and Safety Law
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

    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:eee:jetheo:v:184:y:2019:i:c:s0022053119300808. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/622869 .

    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.