Mickey R M, Greenland S
Department of Mathematics and Statistics, University of Vermont, Burlington 05405.
Am J Epidemiol. 1989 Jan;129(1):125-37. doi: 10.1093/oxfordjournals.aje.a115101.
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).
关于在混杂因素控制中选择变量的恰当方法存在很多争议。许多作者谴责使用任何显著性检验，一些作者鼓励进行此类检验，还有一些作者则提出了一种混合方法。本文展示了对几种混杂因素选择标准进行蒙特卡洛模拟的结果，包括估计值变化和可压缩性检验标准。根据检验规模和效能、偏差、均方误差以及置信区间覆盖率来衡量，对这些方法在关于研究因素效应的推断方面的影响进行了比较。在最佳决策（是否进行调整）并非总是显而易见的情况下，估计值变化标准往往更具优势，不过如果显著性检验方法的显著性水平设定得远高于传统水平（设定为0.20或更高的值），其表现也可以接受。
