School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, USA.
Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA.
BMC Med Res Methodol. 2023 Oct 10;23(1):225. doi: 10.1186/s12874-023-02044-x.
BACKGROUND: INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers. METHODS: In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression. RESULTS: Simulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses. CONCLUSION: While our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error.
背景:INTEROCC 是一项针对职业暴露与脑癌风险的七国队列研究,包括电磁场(EMF)的职业暴露。由于缺乏个体暴露数据,职业暴露矩阵(JEM)可用于构建职业环境中可能的暴露场景。该工具是使用可比工人的各种职业类别的 EMF 暴露的统计摘要构建的。
方法:在这项研究中,我们使用来自 INTEROCC 的加拿大数据,从 JEM 中的三个适当选择的替代物中确定最佳 EMF 暴露替代物/估计值,以及第四个替代物基于通过数值逼近似然函数获得的 Berkson 误差调整。在本文中,我们研究了 JEM 中每个职业的暴露呈伽马分布的情况,作为我们研究团队之前进行的研究中考虑的对数正态分布暴露分布的替代方案。我们还研究了在泊松回归和条件逻辑回归中使用这些替代物和 Berkson 误差调整。
结果:模拟表明,对于非分层分析的 Berkson 误差调整方法提供了在伽马暴露模型下肿瘤发生风险的准确估计。或者,在某些技术假设下,当使用伽马分布作为暴露模型时,算术平均值是最佳替代物。模拟还表明,目前的方法都无法在分层分析中提供准确的风险估计。
结论:虽然我们之前的研究发现几何平均值是最佳的暴露替代物,但本研究表明,最佳替代物取决于暴露模型;在伽马暴露模型的情况下为算术平均值,在对数正态暴露模型的情况下为几何平均值。然而,我们可以为这两种暴露模型中的每一种提供更好的 Berkson 误差调整方法。我们的研究结果为使用 JEM 进行职业暴露评估提供了有用的指导,包括 Berkson 误差调整。
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