Greenland S, Neutra R
Int J Epidemiol. 1980 Dec;9(4):361-7. doi: 10.1093/ije/9.4.361.
Separation of the effects of extraneous variables from the effects of a factor under study (often termed control of confounding) is one of the key prerequisites for validly estimating the magnitude of the study factor's effects. Because of the phenomenon of confounding by indication, confounding of effects of different factors is a common problem in the assessment of medical technology. We give several examples illustrating that the decision of whether a recorded variable is a confounder in a data-set must be decided on the basis of subject-matter knowledge and clinical judgement. There is no alternative to use of such judgement; statistical selection procedures based on significant tests, such as stepwise regression, can be particularly misleading.
将外部变量的影响与所研究因素的影响区分开来（通常称为控制混杂）是有效估计研究因素影响大小的关键前提之一。由于指征性混杂现象，在医疗技术评估中，不同因素影响的混杂是一个常见问题。我们给出几个例子来说明，对于数据集中记录的变量是否为混杂因素的判定必须基于专业知识和临床判断。没有其他办法可以替代这种判断；基于显著性检验的统计选择程序，如逐步回归，可能会特别具有误导性。
