对于混杂因素识别的估计量变化准则，截断值为 10% 是否合适？
Is a cutoff of 10% appropriate for the change-in-estimate criterion of confounder identification?
机构信息
School of Public Health, University of Hong Kong.
出版信息
J Epidemiol. 2014;24(2):161-7. doi: 10.2188/jea.je20130062. Epub 2013 Dec 7.
BACKGROUND
When using the change-in-estimate criterion, a cutoff of 10% is commonly used to identify confounders. However, the appropriateness of this cutoff has never been evaluated. This study investigated cutoffs required under different conditions.
METHODS
Four simulations were performed to select cutoffs that achieved a significance level of 5% and a power of 80%, using linear regression and logistic regression. A total of 10 000 simulations were run to obtain the percentage differences of the 4 fitted regression coefficients (with and without adjustment).
RESULTS
In linear regression, larger effect size, larger sample size, and lower standard deviation of the error term led to a lower cutoff point at a 5% significance level. In contrast, larger effect size and a lower exposure-confounder correlation led to a lower cutoff point at 80% power. In logistic regression, a lower odds ratio and larger sample size led to a lower cutoff point at a 5% significance level, while a lower odds ratio, larger sample size, and lower exposure-confounder correlation yielded a lower cutoff point at 80% power.
CONCLUSIONS
Cutoff points for the change-in-estimate criterion varied according to the effect size of the exposure-outcome relationship, sample size, standard deviation of the regression error, and exposure-confounder correlation.
背景
在使用估计值变化准则时，通常使用 10%的截值来识别混杂因素。然而，该截值的适当性从未得到评估。本研究旨在调查不同条件下所需的截值。
方法
采用线性回归和逻辑回归，进行了 4 次模拟，以选择达到 5%显著性水平和 80%功效的截值。共进行了 10000 次模拟，以获得未调整和调整后的 4 个拟合回归系数的百分比差异。
结果
在线性回归中，较大的效应大小、较大的样本量和较低的误差项标准差导致在 5%的显著性水平下的截值较低。相比之下，较大的效应大小和较低的暴露混杂因素相关性导致在 80%功效下的截值较低。在逻辑回归中，较低的比值比和较大的样本量导致在 5%的显著性水平下的截值较低，而较低的比值比、较大的样本量和较低的暴露混杂因素相关性导致在 80%功效下的截值较低。
结论
估计值变化准则的截值根据暴露结局关系的效应大小、样本量、回归误差的标准差和暴露混杂因素相关性而有所不同。
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