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Risk and Evidence of Bias in Randomized Controlled Trials in Economics

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  • Peter Boone
  • Alex Eble
  • Diana Elbourne
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    Abstract

    The randomized controlled trial (RCT) has been a heavily utilized research tool in medicine for over 60 years. Since the early 2000's, large-scale RCTs have been used in increasingly large numbers in the social sciences to evaluate questions of both policy and theory. The early economics literature on RCTs invokes the medical literature, but seems to ignore a large body of this literature which studies the past mistakes of medical trialists and links poor trial design, conduct and reporting to exaggerated estimates of treatment effects. Using a few consensus documents on these issues from the medical literature, we design a tool to evaluate adequacy of reporting and risk of bias in RCT reports. We then use this tool to evaluate 54 reports of RCTs published in a set of 52 major economics journals between 2001 and 2011 alongside a sample of reports of 54 RCTs published in medical journals over the same time period. We find that economics RCTs fall far short of the recommendations for reporting and conduct put forth in the medical literature, while medical trials stick fairly close to them, suggesting risk of exaggerated treatment effects in the economics literature.

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    Bibliographic Info

    Paper provided by Centre for Economic Performance, LSE in its series CEP Discussion Papers with number dp1240.

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    Date of creation: Sep 2013
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    Handle: RePEc:cep:cepdps:dp1240

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    Web page: http://cep.lse.ac.uk/_new/publications/series.asp?prog=CEP

    Related research

    Keywords: randomized controlled trials; field experiments; bias; treatment effect estimates;

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    1. Abhijit Vinayak Banerjee & Alice H. Amsden & Robert H. Bates & Jagdish Bhagwati & Angus Deaton & Nicholas Stern, 2007. "Making Aid Work," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262026155, December.
    2. Katherine Casey & Rachel Glennerster & Edward Miguel, 2012. "Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan," The Quarterly Journal of Economics, Oxford University Press, vol. 127(4), pages 1755-1812.
    3. Danzon, Patricia M. & Nicholson, Sean & Pereira, Nuno Sousa, 2005. "Productivity in pharmaceutical-biotechnology R&D: the role of experience and alliances," Journal of Health Economics, Elsevier, Elsevier, vol. 24(2), pages 317-339, March.
    4. J. L. Hutton & Paula R. Williamson, 2000. "Bias in meta-analysis due to outcome variable selection within studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 359-370.
    5. Kodrzycki Yolanda K. & Yu Pingkang, 2006. "New Approaches to Ranking Economics Journals," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(1), pages 1-44, August.
    6. Yolanda K. Kodrzycki & Pingkang David Yu, 2005. "New approaches to ranking economics journals," Working Papers, Federal Reserve Bank of Boston 05-12, Federal Reserve Bank of Boston.
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