Department of Mathematics and Statistics, University of Calgary, Calgary, Alberta, Canada.
J Expo Sci Environ Epidemiol. 2011 Mar-Apr;21(2):212-21. doi: 10.1038/jes.2009.74. Epub 2010 Feb 24.
In this paper, we develop models of bias in estimates of exposure-disease associations for epidemiological studies that use group- and individual-based exposure assessments. In a study that uses a group-based exposure assessment, individuals are grouped according to shared attributes, such as job title or work area, and assigned an exposure score, usually the mean of some concentration measurements made on samples drawn from the group. We considered bias in the estimation of exposure effects in the context of both linear and logistic regression disease models, and the classical measurement error in the exposure model. To understand group-based exposure assessment, we introduced a quasi-Berkson error structure that can be justified with a moderate number of exposure measurements from each group. In the quasi-Berkson error structure, the true value is equal to the observed one plus error, and the error is not independent of the observed value. The bias in estimates with individual-based assessment depends on all variance components in the exposure model and is smaller when the between-group and between-subject variances are large. In group-based exposure assessment, group means can be assumed to be either fixed or random effects. Regardless of this assumption, the behavior of estimates is similar: the estimates of regression coefficients were less attenuated with a large sample size used to estimate group means, when between-subject variability was small and the spread between group means was large. However, if groups are considered to be random effects, bias is present, even with large number of measurements from each group. This does not occur when group effects are treated as fixed. We illustrate these models in analyses of the associations between exposure to magnetic fields and cancer mortality among electric utility workers and respiratory symptoms due to carbon black.
本文针对采用群组和个体基础暴露评估的流行病学研究，开发了暴露-疾病关联估计中的偏差模型。在采用群组暴露评估的研究中，个体根据共享属性（如职务或工作区域）进行分组，并分配暴露分数，通常是从群组中抽取的样本的某些浓度测量值的平均值。我们考虑了线性和逻辑回归疾病模型以及暴露模型中经典测量误差背景下的暴露效应估计偏差。为了理解群组暴露评估，我们引入了一种准伯克森误差结构，该结构可以通过从每个群组中获得的适度数量的暴露测量来证明其合理性。在准伯克森误差结构中，真实值等于观测值加上误差，并且误差与观测值不独立。个体基础评估中的估计偏差取决于暴露模型中的所有方差分量，当组间方差和个体间方差较大时，偏差较小。在群组暴露评估中，可以假设群组均值是固定效应或随机效应。无论此假设如何，估计值的行为都是相似的：当用于估计群组均值的样本量较大、个体间变异性较小时，且群组均值之间的差异较大时，回归系数的估计值衰减较小。但是，如果将群组视为随机效应，则即使每个群组的测量值很多，也会存在偏差。当群组效应被视为固定效应时，这种情况不会发生。我们在对电力工人暴露于磁场与癌症死亡率之间的关联以及由于炭黑引起的呼吸道症状的分析中说明了这些模型。
