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Am J Epidemiol. 1989 Jan;129(1):125-37.

The impact of confounder selection criteria on effect estimation.

Author information

  • 1Department of Mathematics and Statistics, University of Vermont, Burlington 05405.

Erratum in

  • Am J Epidemiol 1989 Nov;130(5):1066.


Much controversy exists regarding proper methods for the selection of variables in confounder control. Many authors condemn any use of significance testing, some encourage such testing, and other propose a mixed approach. This paper presents the results of a Monte Carlo simulation of several confounder selection criteria, including change-in-estimate and collapsibility test criteria. The methods are compared with respect to their impact on inferences regarding the study factor's effect, as measured by test size and power, bias, mean-squared error, and confidence interval coverage rates. In situations in which the best decision (of whether or not to adjust) is not always obvious, the change-in-estimate criterion tends to be superior, though significance testing methods can perform acceptably if their significance levels are set much higher than conventional levels (to values of 0.20 or more).

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