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: 6178398
• 负责人: Elizabeth A Lianne Sheppard
• 金额: $ 10.57万
• 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/6178398
• 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.

这项建议侧重于观察性研究设计的发展。 和统计分析方法，以充分利用 对健康影响的研究。我们将重点放在对 关于健康结果的暴露，特别强调环境 曝光。 有两个具体目标。一是制定和评估 模型预测值分组的统计方法。这个目标有两个 组件。一种是由职业流行病学研究推动的， 个人暴露不能被准确地测量。这些方法 开发的重点是对暴露进行回归校正的方法 结合个人和群体信息的估计。我们的目标是 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 将对评估过程中的分组过程进行评估。 替代环境污染暴露措施的普遍做法 对于个人暴露在空气污染中的研究激发了第二个 组件。我们将评估使用代理组曝光的作用 在健康模型中替代个人暴露措施的措施 空气污染的影响，其目标是估计暴露影响 参数。 第二个目的是研究设计和分析的方法。 由多种群研究推动的分组数据结构。这个 第一个子目标专注于基于发病率的综合数据研究 和风险因素调查数据。聚合数据的属性研究 在给定不同的混杂情况下，将评估相对风险参数 系数，公害参数的协变量，以更灵活地适应 变化的数据结构，并适应空间相关性。在 第二个分目标，分组研究个别研究的性质 数据组成部分将由多人群队列研究激励 旨在研究空气污染对健康的影响。我们将解决 群体信息在此类研究中的作用和我们的能力 区分慢性和急性影响。 该方案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 被描述为激励和应用所提议的方法，其他数据集将 在供资期间有资金可用时，可加以利用。这些方法 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 由于暴露在以下环境中的其他环境暴露而产生的健康风险 组偏差和曝光量测量误差。