Invited commentary: variable selection versus shrinkage in the control of multiple confounders.
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.
Greenland
Sander
S
Department of Epidemiology, School of Public Health, University of California, Los Angeles 90095-1772, CA. lesdomes@ucla.edu
D001499:Bayes Theorem; D015982:Bias; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D016016:Proportional Hazards Models; D013223:Statistics as Topic