European Journal of Epidemiology

, Volume 25, Issue 6, pp 361–363 | Cite as

The Janus face of statistical adjustment: confounders versus colliders

COMMENTARY

Abstract

It has long been established that controlling for confounders is essential to delineate the causal relationship between exposure and disease. For this purpose, statistical adjustment is widely used in observational studies. However, many researchers don’t acknowledge the potential pitfalls of statistical adjustment. The aim of the present paper was to demonstrate that statistical adjustment is a double edged sword. By using numerically identical examples, we show that adjustment for a common consequence of the exposure and the outcome can lead to as much bias as absence of necessary adjustment for a confounder.

Keywords

Confounder Collider Bias 

References

  1. 1.
    Pearl J. The art and science of cause and effect. 1996. Available at: http://bayes.cs.ucla.edu/jp_home.html.
  2. 2.
    Hernan MA, Hernandez-Diaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002; Jan 15;155:(2): 176–84.Google Scholar
  3. 3.
    Hernandez-Diaz S, Wilcox AJ, Schisterman EF, et al. From causal diagrams to birth weight-specific curves of infant mortality. Eur J Epidemiol. 2008;23:163–66.CrossRefPubMedGoogle Scholar
  4. 4.
    Berkson J. Limitations of the application of fourfold table analysis to hospital data. Biometrics. 1946;2:47–53.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Imre Janszky
    • 1
  • Anders Ahlbom
    • 2
  • Anna C. Svensson
    • 1
  1. 1.Department of Public Health SciencesKarolinska InstitutetStockholmSweden
  2. 2.Institute of Environmental MedicineKarolinska InstituteStockholmSweden

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