| Summary: | Mickey, R. M. (Dept of Mathematics and Statistics, U. of Vermont, Burlington, VT 05405) and S. Greenland. The impact of confounder selection criteria on effect estimation. Am J Epidemiol 1989;129:125–37. 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 others 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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