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Design sensitivity in observational studies

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  • Paul R. Rosenbaum

Abstract

Outside the field of statistics, the literature on observational studies offers advice about research designs or strategies for judging whether or not an association is causal, such as multiple operationalism or a dose-response relationship. These useful suggestions are typically informal and qualitative. A quantitative measure, design sensitivity, is proposed for measuring the contribution such strategies make in distinguishing causal effects from hidden biases. Several common strategies are then evaluated in terms of their contribution to design sensitivity. A related method for computing the power of a sensitivity analysis is also developed. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Paul R. Rosenbaum, 2004. "Design sensitivity in observational studies," Biometrika, Biometrika Trust, vol. 91(1), pages 153-164, March.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:1:p:153-164
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    Citations

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    Cited by:

    1. Kwonsang Lee & Dylan S. Small & Paul R. Rosenbaum, 2018. "A powerful approach to the study of moderate effect modification in observational studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1161-1170, December.
    2. Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty, 2021. "Increasing power for observational studies of aberrant response: An adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 482-504, July.
    3. Jerzy Michalek, 2012. "Counterfactual impact evaluation of EU rural development programmes - Propensity Score Matching methodology applied to selected EU Member States. Volume 2: A regional approach," JRC Research Reports JRC72060, Joint Research Centre.
    4. Samuel D. Pimentel & Dylan S. Small & Paul R. Rosenbaum, 2016. "Constructed Second Control Groups and Attenuation of Unmeasured Biases," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1157-1167, July.
    5. Xuran Wang & Yang Jiang & Nancy R. Zhang & Dylan S. Small, 2018. "Sensitivity analysis and power for instrumental variable studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1150-1160, December.
    6. Paul R. Rosenbaum, 2015. "Bahadur Efficiency of Sensitivity Analyses in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 205-217, March.
    7. Armstrong, Christopher S. & Guay, Wayne R. & Weber, Joseph P., 2010. "The role of information and financial reporting in corporate governance and debt contracting," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 179-234, December.
    8. Paul R. Rosenbaum, 2013. "Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies," Biometrics, The International Biometric Society, vol. 69(1), pages 118-127, March.
    9. Paul R. Rosenbaum, 2011. "A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies," Biometrics, The International Biometric Society, vol. 67(3), pages 1017-1027, September.
    10. Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.
    11. Frida Skog, 2019. "Sibling Effects on Adult Earnings Among Poor and Wealthy Children Evidence from Sweden," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 12(3), pages 917-942, June.
    12. Nuoo‐Ting Molitor & Nicky Best & Chris Jackson & Sylvia Richardson, 2009. "Using Bayesian graphical models to model biases in observational studies and to combine multiple sources of data: application to low birth weight and water disinfection by‐products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 615-637, June.
    13. Paul R. Rosenbaum & Dylan S. Small, 2017. "An adaptive Mantel–Haenszel test for sensitivity analysis in observational studies," Biometrics, The International Biometric Society, vol. 73(2), pages 422-430, June.

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