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‘Randomisation bias’ in the medical literature: a review

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  • Barbara Sianesi

    (Institute for Fiscal Studies and Institute for Fiscal Studies)

Abstract

Randomised controlled or clinical trials (RCTs) are generally viewed as the most reliable method to draw causal inference as to the effects of a treatment, as they should guarantee that the individuals being compared differ only in terms of their exposure to the treatment of interest. This ‘gold standard’ result however hinges on the requirement that the randomisation device determines the random allocation of individuals to the treatment without affecting any other element of the causal model. This ‘no randomisation bias’ assumption is generally untestable but if violated would undermine the causal inference emerging from an RCT, both in terms of its internal validity and in terms of its relevance for policy purposes. This paper offers a concise review of how the medical literature identifies and deals with such issues.

Suggested Citation

  • Barbara Sianesi, 2016. "‘Randomisation bias’ in the medical literature: a review," IFS Working Papers W16/23, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:16/23
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    File URL: https://www.ifs.org.uk/uploads/publications/wps/WP201623.pdf
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    References listed on IDEAS

    as
    1. Glasgow, R.E. & Vogt, T.M. & Boles, S.M., 1999. "Evaluating the public health impact of health promotion interventions: The RE-AIM framework," American Journal of Public Health, American Public Health Association, vol. 89(9), pages 1322-1327.
    2. Hunt Allcott, 2012. "Site Selection Bias in Program Evaluation," NBER Working Papers 18373, National Bureau of Economic Research, Inc.
    3. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    4. Bower, Peter & King, Michael & Nazareth, Irwin & Lampe, Fiona & Sibbald, Bonnie, 2005. "Patient preferences in randomised controlled trials: Conceptual framework and implications for research," Social Science & Medicine, Elsevier, vol. 61(3), pages 685-695, August.
    5. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    7. Barbara Sianesi, 2014. "Dealing with randomisation bias in a social experiment: the case of ERA," IFS Working Papers W14/10, Institute for Fiscal Studies.
    8. Joseph Hotz, V. & Imbens, Guido W. & Mortimer, Julie H., 2005. "Predicting the efficacy of future training programs using past experiences at other locations," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 241-270.
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    randomised trials; medical;

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