Ortega-Villa Ana Maria, Liu Danping, Ward Mary H, Albert Paul S
Biostatistics Research Branch, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland.
Biostatistics Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; and.
Environ Epidemiol. 2020 Dec 16;5(1):e116. doi: 10.1097/EE9.0000000000000116. eCollection 2021 Feb.
In environmental epidemiology, it is of interest to assess the health effects of environmental exposures. Some exposure analytes present values that are below the laboratory limit of detection (LOD). There have been many methods proposed for handling this issue to incorporate exposures subject to LOD in risk modeling using logistic regression. We present a fresh look at proposed methods to handle exposure analytes that present values that are below the LOD.
We performed comparisons through an extensive simulation study and a cancer epidemiology example. The methods we considered were a maximum-likelihood approach, multiple imputation, Cox regression, complete case analysis, filling in values below the LOD with , and a missing indicator method.
We found that the logistic regression coefficient associated with the exposure (subject to LOD) can be severely biased when underlying assumptions are not met, even with a relatively small proportion (under 20%) of measurements below the LOD.
We propose the use of a simple method where the relationship between the analyte and disease risk is modeled only above the detection limit with an intercept term at the LOD and a slope parameter, which makes no assumptions about what happens below the LOD. In most practical situations, our results suggest that this simple method may be the best choice for analyzing analytes with detection limits.
在环境流行病学中，评估环境暴露对健康的影响很有意义。一些暴露分析物呈现出低于实验室检测限（LOD）的值。已经提出了许多方法来处理这个问题，以便在使用逻辑回归的风险建模中纳入受检测限影响的暴露情况。我们重新审视了为处理呈现低于检测限值的暴露分析物而提出的方法。
我们通过广泛的模拟研究和一个癌症流行病学实例进行了比较。我们考虑的方法有最大似然法、多重填补、Cox回归、完整病例分析、用 填充低于检测限的值以及缺失指示法。
我们发现，当基本假设不满足时，与暴露（受检测限影响）相关的逻辑回归系数可能会有严重偏差，即使低于检测限的测量比例相对较小（低于20%）。
我们建议使用一种简单方法，即仅在检测限以上对分析物与疾病风险之间的关系进行建模，在检测限处设置一个截距项和一个斜率参数，而不对检测限以下发生的情况做任何假设。在大多数实际情况下，我们的结果表明，这种简单方法可能是分析有检测限的分析物的最佳选择。
