IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2202.06191.html
   My bibliography  Save this paper

Exploration and Incentivizing Participation in Clinical Trials

Author

Listed:
  • Yingkai Li
  • Aleksandrs Slivkins

Abstract

Participation incentives a well-known issue inhibiting clinical trials. We frame this issue as a non-standard exploration-exploitation tradeoff: the trial would like to explore as uniformly as possible, whereas each patient prefers "exploitation", i.e., treatments that seem best. We incentivize participation by leveraging information asymmetry between the trial and the patients. We measure statistical performance via worst-case estimation error under adversarially generated outcomes, a standard objective for clinical trials. We obtain a near-optimal solution in terms of this objective: an incentive-compatible mechanism with a particular guarantee, and a nearly matching impossibility result for any incentive-compatible mechanism. Our results extend to heterogeneous agents.

Suggested Citation

  • Yingkai Li & Aleksandrs Slivkins, 2022. "Exploration and Incentivizing Participation in Clinical Trials," Papers 2202.06191, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2202.06191
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2202.06191
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Esponda, Ignacio & Pouzo, Demian, 2021. "Equilibrium in misspecified Markov decision processes," Theoretical Economics, Econometric Society, vol. 16(2), May.
    2. Ignacio Esponda & Demian Pouzo, 2016. "Berk–Nash Equilibrium: A Framework for Modeling Agents With Misspecified Models," Econometrica, Econometric Society, vol. 84, pages 1093-1130, May.
    3. Martin Hellmich, 2001. "Monitoring Clinical Trials with Multiple Arms," Biometrics, The International Biometric Society, vol. 57(3), pages 892-898, September.
    4. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    5. Emir Kamenica, 2019. "Bayesian Persuasion and Information Design," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 249-272, August.
    6. Yeon-Koo Che & Johannes Hörner, 2018. "Recommender Systems as Mechanisms for Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 871-925.
    7. Yingkai Li & Harry Pei, 2020. "Misspecified Beliefs about Time Lags," Papers 2012.07238, arXiv.org.
    8. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    9. Drew Fudenberg & Giacomo Lanzani & Philipp Strack, 2021. "Limit Points of Endogenous Misspecified Learning," Econometrica, Econometric Society, vol. 89(3), pages 1065-1098, May.
    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. J. Aislinn Bohren & Daniel N. Hauser, 2023. "Behavioral Foundations of Model Misspecification," PIER Working Paper Archive 23-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Anand Kalvit & Aleksandrs Slivkins & Yonatan Gur, 2024. "Incentivized Exploration via Filtered Posterior Sampling," Papers 2402.13338, arXiv.org.
    3. Fudenberg, Drew & Gao, Ying & Pei, Harry, 2022. "A reputation for honesty," Journal of Economic Theory, Elsevier, vol. 204(C).
    4. Roesler, Anne-Katrin & Deb, Rahul, 2021. "Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness," CEPR Discussion Papers 16206, C.E.P.R. Discussion Papers.
    5. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    6. Fan, Zhongjie & Tang, Dunzhe, 2023. "Financial fragility and information design," Economics Letters, Elsevier, vol. 232(C).
    7. Eduardo Perez‐Richet & Vasiliki Skreta, 2022. "Test Design Under Falsification," Econometrica, Econometric Society, vol. 90(3), pages 1109-1142, May.
    8. Andrew T Little, 2023. "Bayesian explanations for persuasion," Journal of Theoretical Politics, , vol. 35(3), pages 147-181, July.
    9. Shih-Tang Su & Vijay G. Subramanian, 2022. "Order of Commitments in Bayesian Persuasion with Partial-informed Senders," Papers 2202.06479, arXiv.org.
    10. Razin, Ronny & Levy, Gilat & Young, Alwyn, 2022. "Misspecified politics and the recurrence of populism," LSE Research Online Documents on Economics 112544, London School of Economics and Political Science, LSE Library.
    11. Jonas Hedlund & Allan Hernández-Chanto & Carlos Oyarzún, 2021. "Contagion Management through Information Disclosure," Discussion Papers Series 651, School of Economics, University of Queensland, Australia.
    12. Shih-Tang Su & Vijay G. Subramanian & Grant Schoenebeck, 2021. "Bayesian Persuasion in Sequential Trials," Papers 2110.09594, arXiv.org, revised Nov 2021.
    13. Terstiege, Stefan & Wasser, Cédric, 2023. "Experiments versus distributions of posteriors," Mathematical Social Sciences, Elsevier, vol. 125(C), pages 58-60.
    14. Bowen, T. Renee & Galperti, Simone & Dmitriev, Danil, 2021. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," CEPR Discussion Papers 15789, C.E.P.R. Discussion Papers.
    15. Eitan Sapiro-Gheiler, 2021. "Persuasion with Ambiguous Receiver Preferences," Papers 2109.11536, arXiv.org, revised Aug 2023.
    16. Heski Bar‐Isaac & Clare Leaver, 2022. "Training, Recruitment, and Outplacement as Endogenous Adverse Selection," Economica, London School of Economics and Political Science, vol. 89(356), pages 849-861, October.
    17. Ganesh Iyer & Zemin (Zachary) Zhong, 2022. "Pushing Notifications as Dynamic Information Design," Marketing Science, INFORMS, vol. 41(1), pages 51-72, January.
    18. Paul Heidhues & Botond Koszegi & Philipp Strack, 2023. "Misinterpreting Yourself," Cowles Foundation Discussion Papers 2378, Cowles Foundation for Research in Economics, Yale University.
    19. Serena Wang & Michael I. Jordan & Katrina Ligett & R. Preston McAfee, 2024. "Information Elicitation in Agency Games," Papers 2402.14005, arXiv.org, revised Apr 2024.
    20. Raphaela Hennigs, 2021. "Conflict prevention by Bayesian persuasion," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 23(4), pages 710-731, August.

    More about this item

    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:arx:papers:2202.06191. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.