Measurement Errors in Environmental Epidemiology

摘要 在几乎所有环境和流行病学研究中，暴露测量误差都是偏倚的可能来源。 研究，通常会导致低估相对风险，并失去检测影响的统计能力。 在本项目的上一个周期中，我们扩展了回归校准方法， 多变量回归模型（包括考克斯模型和logistic模型）中的测量误差， 适应环境流行病学中遇到的研究设计和数据结构。我们 在一些关于环境暴露对健康影响的出版物中介绍了这些新方法， 内毒素、甲基叔丁基醚、铅和室内NO2，并开发了公开可用的软件。在 在下一个周期，我们将集中研究空气污染流行病学问题，特别是慢性 颗粒暴露和元素碳对全因死亡率、心血管死亡率和 肺癌死亡率在组建了一个由主要统计学家组成的跨学科小组之后， 科学家和环境流行病学家来自世界各地，我们将制定方法，以调整 适用于六个城市前瞻性队列设计的考克斯回归模型的测量误差 研究、护士健康研究、荷兰队列研究和MESA-Air与累积和12 月平均暴露指标。将认真关注围绕着 暴露时间和暴露度量的剂量-反应曲线的潜在非线性， 特别地，消除了由于曝光测量误差而导致的这些特征的量化中的偏差。的 NHS和MESA-AIR中可用的生物标志物数据将用于改进暴露验证。User- 将在网上发布友好的软件，以便于大规模应用新方法。

Abstract Exposure measurement error is a likely source of bias in nearly all environmental and epidemiological studies, typically leading to under-estimation of relative risks and loss of statistical power to detect effects. In the previous cycle of this project, we extended the regression calibration method for adjustment for measurement error in multivariate regression models, including Cox models and logistic models, to accommodate the study designs and data structures encountered in environmental epidemiology. We featured these new methods in a number of publications on the health effects of environmental exposure to endotoxin, methyl tert-butyl ether, lead, and indoor NO2, and developed publicly available software. In the next cycle of this project, we will focus on issues in air pollution epidemiology - in particular, the chronic effects of particulate exposure and elemental carbon on all-cause mortality, cardio-vascular mortality and lung cancer mortality. Having assembled an inter-disciplinary team of leading statisticians, environmental scientists and environmental epidemiologists from around the world, we will develop methods to adjust for measurement error in Cox regression models suitable for the prospective cohort designs of the Six Cities Study, Nurses' Health Study, Netherlands Cohort Study, and MESA-Air in relation to cumulative and 12 month running average exposure metrics. Careful attention will be paid to the important issues surrounding timing of exposure and potential non-linearity of the dose-response curves of the exposure metrics, in particular, removing bias in the quantification of these features due to exposure measurement error. The biomarker data available in NHS and MESA-AIR will be used to improve the exposure validation. User- friendly software will be posted on the web, facilitating widescale application of the new methods.