Measurement Errors in Environmental Epidemiology

摘要 在几乎所有的环境和流行病学中，暴露测量误差都可能是产生偏差的来源 研究，通常会导致低估相对风险和丧失检测效果的统计能力。 在本项目的前一个周期中，我们扩展了用于调整的回归校正方法 多元回归模型中的测量误差，包括COX模型和Logistic模型 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 在这个项目的下一个周期，我们将集中讨论空气污染流行病学的问题，特别是慢性 颗粒物暴露和元素碳对全因死亡率、心脑血管死亡率和 肺癌死亡率。组建了一支由领先的统计学家组成的跨学科团队，环境 来自世界各地的科学家和环境流行病学家，我们将开发方法来适应 适用于六城市前瞻性队列设计的COX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 月运行平均曝险指标。我们将认真关注与此相关的重要问题 照射的时间和照射指标的剂量-反应曲线的潜在非线性， 具体地说，消除了由于曝光测量误差而在量化这些特征时的偏差。这个 NHS和MESA-AIR提供的生物标记物数据将用于改进暴露验证。用户- 将在网上发布友好的软件，促进新方法的广泛应用。

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.