Abstract:
: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.
journal_name
Am J Epidemioljournal_title
American journal of epidemiologyauthors
Greenland Sdoi
10.1093/aje/kwm355subject
Has Abstractpub_date
2008-03-01 00:00:00pages
523-9; discussion 530-1issue
5eissn
0002-9262issn
1476-6256pii
kwm355journal_volume
167pub_type
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