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Identifying Placebo Effects with Data from Clinical Trials

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  • Anup Malani

Abstract

A medical treatment is said to have placebo effects if patients who are optimistic about the treatment respond better to the treatment. This paper proposes a simple test for placebo effects. Instead of comparing the treatment and control arms of a single trial, one should compare the treatment arms of two trials with different probabilities of assignment to treatment. If there are placebo effects, patients in the higher-probability trial will experience better outcomes simply because they believe that there is a greater chance of receiving treatment. This paper finds evidence of placebo effects in trials of antiulcer and cholesterol-lowering drugs.

Suggested Citation

  • 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.
  • Handle: RePEc:ucp:jpolec:v:114:y:2006:i:2:p:236-256
    DOI: 10.1086/500279
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    Cited by:

    1. Roberto Ippoliti, 2013. "The market of human experimentation," European Journal of Law and Economics, Springer, vol. 35(1), pages 61-85, February.
    2. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    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. van den Berg, Gerard J., 2007. "An Economic Analysis of Exclusion Restrictions for Instrumental Variable Estimation," IZA Discussion Papers 2585, Institute of Labor Economics (IZA).
    5. Jens Ludwig & Jeffrey R. Kling & Sendhil Mullainathan, 2011. "Mechanism Experiments and Policy Evaluations," Journal of Economic Perspectives, American Economic Association, vol. 25(3), pages 17-38, Summer.
    6. 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.
    7. Chemla, Gilles & Hennessy, Christopher, 2016. "Bayesian Expectancy Invalidates Double-Blind Randomized Controlled Medical Trials," CEPR Discussion Papers 11360, C.E.P.R. Discussion Papers.
    8. 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.
    9. 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.
    10. Adam Leive & Thomas Stratmann, 2015. "Do national cancer screening guidelines reduce mortality?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(4), pages 1075-1095, October.
    11. Steven D. Levitt & John A. List, 2011. "Was There Really a Hawthorne Effect at the Hawthorne Plant? An Analysis of the Original Illumination Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 224-238, January.
    12. 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.
    13. Roberto Ippoliti, 2012. "Making profits working on patients’ expectations, a behavioral analysis of pharmaceutical clinical research," Journal of Bioeconomics, Springer, vol. 14(3), pages 217-241, October.
    14. Armando Franco & Dana P. Goldman & Adam Leive & Daniel McFadden, 2017. "A Cautionary Tale in Comparative Effectiveness Research: Pitfalls and Perils of Observational Data Analysis," NBER Chapters, in: Measuring and Modeling Health Care Costs, pages 55-80, National Bureau of Economic Research, Inc.
    15. Chemla, Gilles & Hennessy, Christopher A., 2019. "Controls, belief updating, and bias in medical RCTs," Journal of Economic Theory, Elsevier, vol. 184(C).

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