Send to

Choose Destination
See comment in PubMed Commons below
Am J Epidemiol. 2008 Mar 1;167(5):523-9; discussion 530-1. doi: 10.1093/aje/kwm355. Epub 2008 Jan 27.

Invited commentary: variable selection versus shrinkage in the control of multiple confounders.

Author information

Department of Epidemiology, School of Public Health, University of California, Los Angeles 90095-1772, CA.

Erratum in

  • Am J Epidemiol. 2008 May 1;167(9):1142.


After screening out inappropriate or doubtful covariates on the basis of background knowledge, one may still be left with many potential confounders. It is then tempting to use statistical variable-selection methods to reduce the number used for adjustment. Nonetheless, there is no agreement on how selection should be conducted, and it is well known that conventional selection methods lead to confidence intervals that are too narrow and p values that are too small. Furthermore, theory and simulation evidence have found no selection method to be uniformly superior to adjusting for all well-measured confounders. Nonetheless, control of all measured confounders can lead to problems for conventional model-fitting methods. When these problems occur, one can apply modern techniques such as shrinkage estimation, exposure modeling, or hybrids that combine outcome and exposure modeling. No selection or special software is needed for most of these techniques. It thus appears that statistical confounder selection may be an unnecessary complication in most regression analyses of effects.

[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems
    Loading ...
    Support Center