METHODS FOR USING GROUP INFORMATION IN EPIDEMIOLOGY

在流行病学中使用群体信息的方法

• 批准号: 2018665
• 负责人: Elizabeth A Lianne Sheppard
• 金额: $ 9.77万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-01 至 2002-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2018665
• 关键词: air pollution; cancer risk; clinical research; data collection methodology /evaluation; disease /disorder proneness /risk; environmental health; epidemiology; geographic site; human data; human population study; mathematical model; method development; occupational stressor; outcomes research; statistics /biometry

This proposal focuses on the development of observational study designs and statistical analysis methods to capitalize on group formation in the study of health effects. We focus on estimation of the effect of exposures on health outcomes with particular emphasis environmental exposures. There are two specific aims. The first is to develop and evaluate statistical methods for grouping in model predictors. This aim is has two components. One is motivated by occupational epidemiologic studies where individual exposures cannot be accurately measured. The methods development focuses on a regression calibration approach to exposure estimation that combines individual and group information. The goal is to minimize bias and maximize the precision of the exposure effect (or relative risk) parameter estimate in an exposure-response model. The role of the grouping process in the estimation procedure will be evaluated. The common practice of substituting ambient pollution exposure measures for individual exposures in air pollution studies motivates the second component. We will evaluate the role of using proxy group exposure measures in place of individual exposure measures in models of the health effects of air pollution where the goal is estimation of exposure effect parameters. The second aim is to research methods for the design and analysis of grouped data structures as motivated by multipopulation studies. The first subaim concentrates on aggregate data studies based on disease rates and risk factor survey data. Properties of the aggregate data study relative risk parameter will be evaluated given varying confounding factor, covariate of the nuisance parameters, to more flexibly accommodate varying data structures, and to accommodate spatial dependence. in the second subaim, study of the properties of individual studies with grouped data components will be motivated by multipopulation cohort studies designed to study of health effects of air pollution. We will address the role of between group information in such studies and our ability to differentiate between chronic and acute effects. Methods used in this proposal include clear problem definition, asymptotic statistical theory, and Monte Carlo simulation. three datasets are described to motivate and apply the proposed methods, other datasets will be utilized as they become available over the funding period. The methods are relevant to a wide range of environmental health applications, including occupational studies, air pollution research, and the assessment of health risks due to other environmental exposures exhibiting between- group variation and exposure measurement error.

该提案侧重于观察性研究设计的开发 和统计分析方法，以利用群体形成 对健康影响的研究。 我们重点关注效果的估计 对健康结果的暴露，特别强调环境 曝光。 有两个具体目标。 首先是开发和评估 模型预测变量分组的统计方法。 这个目标有两个 成分。 其一是受到职业流行病学研究的推动，其中 无法准确测量个人暴露情况。方法 开发重点是曝光回归校准方法 结合个人和群体信息的估计。 目标是 最大限度地减少偏差并最大限度地提高曝光效果的精度（或 相对风险）暴露-反应模型中的参数估计。角色 将评估估计程序中的分组过程。 替代环境污染暴露措施的常见做法 空气污染研究中的个人暴露激发了第二个 成分。 我们将评估使用代理组曝光的作用 健康模型中替代个人暴露措施的措施 空气污染的影响，目标是估计暴露效应 参数。 第二个目标是研究设计和分析的方法。 由多群体研究驱动的分组数据结构。 这 第一个子目标专注于基于疾病发生率的汇总数据研究 和风险因素调查数据。 聚合数据研究的属性 将在考虑不同混杂因素的情况下评估相对风险参数 因素，干扰参数的协变量，以更灵活地适应 不同的数据结构，并适应空间依赖性。 在 第二个子目标，通过分组研究个体研究的特性 数据成分将受到多人群队列研究的推动 旨在研究空气污染对健康的影响。 我们将解决 此类研究中群体信息之间的作用以及我们的能力 区分慢性和急性影响。 该提案中使用的方法包括明确的问题定义、渐近 统计理论和蒙特卡罗模拟。 三个数据集是 描述以激励和应用所提出的方法，其他数据集将 在资助期内可用时予以利用。 方法 与广泛的环境健康应用相关， 包括职业研究、空气污染研究和评估 由于其他环境暴露而造成的健康风险表现出以下特征： 群体变异和暴露测量误差。