Statistical Methods to Account for Exposure Uncertainty in Environmental Epidemiology

 描述（由申请人提供）：暴露测量误差是几乎所有环境健康研究中偏倚的一个可能来源，通常会导致相对风险的低估和检测效应的统计功效的丧失。在这项拟议的研究中，我们将采取生命历程的方法，与NIEHS的战略重点一致，重点关注环境健康的几个关键领域的方法学需求，包括空气污染成分的影响和社区环境对心血管疾病及其前兆和后果的影响，包括全因死亡率，肥胖，2型糖尿病和亚临床心血管生物标志物。组建了一支由领先的理论和应用统计学家，环境流行病学家和环境暴露评估专家组成的强大的多学科团队，与NIEHS的另一个战略目标相一致，我们将为紧迫的环境卫生政策重要性的新领域做出重大贡献，主要侧重于开发方法，以准确量化复杂的单一和多重影响，跨空间和时间的同时曝光效应，响应另一个NIEHS战略优先事项，减少（如果不能消除）由于存在大量曝光测量误差而存在的偏差和效率损失。在这项工作中，将仔细注意消除偏见，由于空间和时间的混杂，以及调整室内空气污染源的混杂。目前可用的验证数据的空气污染成分和附近环境的功能将被组装和使用，以开发测量误差模型的个人暴露测量环境暴露适合手头的数据。混合纵向和考克斯生存数据回归模型将作为分析框架的基础。实施这些方法的方便用户的软件将张贴在网上，以便利将新方法广泛应用于各种环境卫生问题。

 DESCRIPTION (provided by applicant): Exposure measurement error is a likely source of bias in nearly all environmental health studies, typically leading to an under-estimation of relative risks and a loss of statistical power to detect effects. In this proposed research, we wil take a life course approach, as consistent with NIEHS strategic priorities, focusing on methodological needs in several critical areas of environmental health, including the effects of constituents of air pollution and of aspects of the neighborhood environment on cardiovascular disease and its precursors and consequences, including all-cause mortality, obesity, type 2 diabetes and subclinical cardiovascular biomarkers. Having assembled a strong multi-disciplinary team of leading theoretical and applied statisticians, environmental epidemiologists and environmental exposure assessment experts, consistent with another NIEHS strategic objective, we will make significant contributions to novel areas of pressing environmental health policy importance, with a major focus on developing methods to accurately quantify the effects of complex single and multiple, simultaneous exposure effects across space and time, responding to another NIEHS strategic priority, reducing if not eliminating the bias and loss of efficiency otherwise present due to the presence of substantial exposure measurement error. In this work, careful attention will be paid to removing bias due to spatial and temporal confounding as well as to adjusting for confounding by indoor sources of air pollution. Currently available validation data on air pollution constituents and on features of the neighborhood environment will be assembled and used to develop measurement error models relating personal exposure to measured ambient exposure as suitable for the data at hand. Mixed longitudinal and Cox survival data regression models will underlie the analytic framework. User-friendly software implementing the methods will be posted on the web, facilitating wide-scale application of the new methods to a broad range of environmental health problems.