Log-transformation of Independent Variables: Must We?
Log-transformation of Independent Variables: Must We?
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DOI：
10.1097/ede.0000000000001534
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发表时间：
2022-11-01
期刊：
影响因子：
5.4
通讯作者：
Keil, Alexander P.
中科院分区：
文献类型：
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作者：
Choi, Giehae;Buckley, Jessie P.;Kuiper, Jordan R.;Keil, Alexander P.
流行病学研究经常使用生物标志物来量化暴露，这些生物标志物在统计上通常具有不对称的分布。虽然在线性回归中将生物标记物用作自变量时不需要正态假设，但对生物标记物浓度进行对数变换已成为一种常见的做法。这种转变可以受到对非线性剂量-反应关系或异常值的关注，然而，这种转变并不总是减少偏差。在这项研究中，我们使用模拟评估了决定对自变量进行对数转换的动机的有效性，考虑了八种可能导致X偏斜和Y正常的情景。我们的模拟研究表明：(1)如果暴露的偏斜不是由偏差因素(例如，测量误差)引起的，则具有最佳整体模型拟合的分析方法最好地反映了潜在的结果生成方法，并且最小偏倚，无论X的偏斜程度如何；(2)如果暴露的偏斜是偏差因素的结果，则所有估计都是偏向的。此外，我们还说明了使用NHANES来确定是否需要对自变量进行转换的过程。我们的研究和建议将暴露分布的形状与对数变换的决定分开，这可能有助于研究人员计划使用生物标记物或其他倾斜的自变量进行分析。
Epidemiologic studies often quantify exposure using biomarkers, which commonly have statistically skewed distributions. Although normality assumption is not required if the biomarker is used as an independent variable in linear regression, it has become common practice to log-transform the biomarker concentrations. This transformation can be motivated by concerns for non-linear dose–response relationship or outliers, however, such transformation may not always reduce bias. In this study, we evaluated the validity of motivations underlying the decision to log-transform an independent variable using simulations, considering eight scenarios that can give rise to skewed X and normal Y. Our simulation study demonstrates that (1) if the skewness of exposure did not arise from a biasing factor (e.g., measurement error), the analytic approach with the best overall model fit best reflected the underlying outcome generating methods and was least biased, regardless of the skewness of X and (2) all estimates were biased if the skewness of exposure was a consequence of a biasing factor. We additionally illustrate a process to determine whether the transformation of an independent variable is needed using NHANES. Our study and suggestion to divorce the shape of the exposure distribution from the decision to log-transform it may aid researchers in planning for analysis using biomarkers or other skewed independent variables.