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Hierarchical Bayesian Analysis of Complex Sample Survey Data

复杂样本调查数据的分层贝叶斯分析

基本信息

批准号：
8193219
负责人：
MICHAEL R. ELLIOTT
金额：
$ 28.91万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-17 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8193219
关键词：
Accounting Acute Address Adult American Area Automobile Driving Bayesian Analysis Bayesian Method Behavioral Birth Weight Censuses Child Complex Computer software County Data Data Set Data Sources Dimensions Elderly Equilibrium Exhibits Health Health Surveys Malignant Neoplasms Measurement Measures Methodology Methods Modeling National Health Interview Survey National Health and Nutrition Examination Survey Outcome Policy Research Population Population Statistics Prevalence Probability Procedures Process Property Public Health Race Records Research Risk Factors Sample Size Sampling Sampling Errors Scheme Selection Bias Source Stratification Surveys Systematic Bias Techniques Technology Time Weight Work base cardiovascular risk factor design ethnic minority population improved indexing interest method development model development mortality population based public health relevance simulation statistics user friendly software

项目摘要

DESCRIPTION (provided by applicant): The proposed research will use hierarchical Bayesian modeling to tackle three interrelated problems in the analysis of population-based survey data: accounting for unequal probabilities of inclusion due to sample design or post-sampling non-response; accounting for non-ignorable missingness in item-level data; and combining information from multiple complex survey data sets to obtain more accurate and efficient estimates of the population quantities. We intend to develop robust models that can provide "data- driven" weight trimming procedures for a general class of population statistics under a variety of sample designs; develop selection models that accommodate non-ignorable missingness mechanisms in the context of complex survey designs; and develop methods to combine data from multiple surveys by creating synthetic populations from each survey and then combining these populations to develop estimates. While our methods will be applicable to a wide variety of analytic procedures, we will focus on small area or small domain estimation in particular, since the issues that this proposal intends to address are often most acute in the setting. Domain estimators with highly variable weights can have poor mean square error properties. Associations between nonignorable nonresponse and areas/domains can make between-domain comparisons unreliable. Small samples in a given domain in one survey can be compensated by data from other surveys, if correct procedures are in place to account for complex sample design, as well as the possibility of non-response bias and measurement error. We will consider three major applications: analyses to determine associations between birth weight and cardiovascular risk factors in children using the National Health and Nutrition Examination Survey, to determine the prevalence of cancer behavioral risk factors among adults by combining data from the Behavioral Risk Factor Surveillance Survey and the National Health Interview Survey, and to explore mortality compression among the elderly in the Americans Changing Lives panel survey. Analyses will focus on small domains (race/ethnic minorities, and counties/states, as examples). Though the method is motivated from a Bayesian perspective, the results will be evaluated from the design-based perspective using analytical and simulation techniques. We will also focus on developing user-friendly software to implement the new methods. PUBLIC HEALTH RELEVANCE: In an increasingly diverse nation, the need is increasing to target public health studies and delivery to small areas, be they geographic or demographic (such as ethnic minorities). Health surveys are a rich source of data for such efforts, but methods for extracting information about small areas remain undeveloped. The proposed work will develop new methods for dealing with some of the problems that small area estimation poses, including unstable estimates due to small sample sizes and unequal probabilities of selection, and biased estimates due to differences between people who chose to participate in the surveys and those who refused or could not be contacted. The work can also improve the efficient use of data currently collected by developing new ways of combining data from multiple surveys.

描述（由申请人提供）：拟议的研究将使用分层贝叶斯模型来解决基于人口的调查数据分析中的三个相互关联的问题：由于样本设计或抽样后无反应而导致的不平等纳入概率；考虑项目级数据中不可忽略的缺失；并结合多个复杂调查数据集的信息，以获得更准确和有效的人口数量估计。我们打算开发健壮的模型，可以在各种样本设计下为一般类别的人口统计提供“数据驱动”的权重修剪程序；开发选择模型，以适应复杂调查设计中不可忽视的缺失机制；并开发方法，通过从每个调查中创建合成人口，然后将这些人口结合起来进行估计，从而将来自多个调查的数据结合起来。虽然我们的方法将适用于各种各样的分析过程，但我们将特别关注小区域或小领域的估计，因为本建议打算解决的问题通常在设置中最尖锐。具有高度可变权值的域估计器可能具有较差的均方误差特性。不可忽略的非响应和区域/域之间的关联可以使域之间的比较不可靠。如果采用正确的程序来考虑复杂的样本设计，以及可能存在的无反应偏差和测量误差，则可以用其他调查的数据来补偿一次调查中给定领域的小样本。我们将考虑三个主要应用：使用国家健康和营养检查调查来分析确定儿童出生体重和心血管危险因素之间的关系，通过结合行为危险因素监测调查和国家健康访谈调查的数据来确定成人中癌症行为危险因素的患病率，以及在美国人改变生活小组调查中探索老年人的死亡率压缩。分析将集中在小领域（如种族/少数民族，县/州）。虽然该方法是从贝叶斯的角度出发的，但结果将从基于设计的角度使用分析和模拟技术进行评估。我们也会专注于开发用户友好的软件来实施这些新方法。公共卫生相关性：在一个日益多样化的国家，越来越需要将公共卫生研究的目标和成果提供给小区域，无论是地理上的还是人口上的（如少数民族）。健康调查是此类努力的丰富数据来源，但提取小地区信息的方法仍未开发。拟议的工作将开发新的方法来处理小面积估计所带来的一些问题，包括由于小样本量和不平等的选择概率而导致的不稳定估计，以及由于选择参加调查的人与拒绝或无法联系的人之间的差异而导致的有偏见的估计。这项工作还可以通过开发结合多个调查数据的新方法，提高目前收集数据的有效利用。