Sun G W, Shook T L, Kay G L
Heart Institute, Good Samaritan Hospital, Los Angeles, California 90017-2395, USA.
J Clin Epidemiol. 1996 Aug;49(8):907-16. doi: 10.1016/0895-4356(96)00025-x.
The use of bivariable selection (BVS) for selecting variables to be used in multivariable analysis is inappropriate despite its common usage in medical sciences. In BVS, if the statistical p value of a risk factor in bivariable analysis is greater than an arbitrary value (often p = 0.05), then this factor will not be allowed to compete for inclusion in multivariable analysis. This type of variable selection is inappropriate because the BVS method wrongly rejects potentially important variables when the relationship between an outcome and a risk factor is confounded by any confounder and when this confounder is not properly controlled. This article uses both hypothetical and actual data to show how a nonsignificant risk factor in bivariable analysis may actually be a significant risk factor in multivariable analysis if confounding is properly controlled. Furthermore, problems resulting from the automated forward and stepwise modeling with or without the presence of confounding are also addressed. To avoid these improper procedures and deficiencies, alternatives in performing multivariable analysis, including advantages and disadvantages of the BVS method and automated stepwise modeling, are reviewed and discussed.
尽管双变量选择（BVS）在医学科学中被广泛使用，但用于选择多变量分析中要使用的变量是不合适的。在BVS中，如果双变量分析中一个风险因素的统计p值大于一个任意值（通常p = 0.05），那么这个因素将不被允许参与多变量分析的入选竞争。这种变量选择方式是不合适的，因为当一个结局与一个风险因素之间的关系被任何混杂因素混淆且该混杂因素未得到适当控制时，BVS方法会错误地拒绝潜在重要的变量。本文使用假设数据和实际数据来表明，如果混杂因素得到适当控制，双变量分析中无统计学意义的风险因素在多变量分析中实际上可能是有统计学意义的风险因素。此外，还讨论了在有或没有混杂因素情况下自动向前和逐步建模所产生的问题。为避免这些不当程序和缺陷，本文回顾并讨论了进行多变量分析的替代方法，包括BVS方法和自动逐步建模的优缺点。
