Model-Based Methods for the Analyses of Weighted Data

基于模型的加权数据分析方法

• 批准号: 7143957
• 负责人: MICHAEL R. ELLIOTT
• 金额: $ 13.86万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-10 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7143957
• 关键词: cancer risk; cardiovascular disorder; child (0-11); clinical research; health surveys; human data; interview; mathematical model; method development; statistics /biometry; vehicular accident

DESCRIPTION (provided by applicant): The purpose of the proposed research is to develop methodologies for the analysis of data with highly differential probabilities of inclusion by developing model-based methods that combine the robustness of the design approach with the optimality obtainable through a model. In unequal-probability-of-selection sample designs often found in national, population-based, complex-sample-design health surveys, correlations between the probability of selection and the sampled data can induce bias. Weights equal to the inverse of the probability of selection are often used to counteract this bias. Highly disproportional sample designs have large weights, which can introduce unnecessary variability in statistics such as the population mean estimate. Weight trimming reduces large weights to a fixed cutpoint value and adjusts weights below this value to maintain the untrimmed weight sum. This reduces variability at the cost of introducing some bias. Standard approaches are not "data-driven": they do not use the data to make the appropriate bias-variance tradeoff, or else do so in a highly inefficient fashion. We propose to develop model-based methods for "weight trimming" to supplement standard, ad-hoc design-based methods in disproportional probability-of-inclusion designs where variances due to sample weights exceeds bias correction. We will also consider the use of these models to estimate population parameters in linear and generalized linear regression models. We develop these models in the context of stratified and poststratified known-probability sample designs, and extend their use into more general multistage cluster sample designs. We plan to develop Bayesian models, as we believe that they offer both theoretical and practical advantages to traditional frequentist analyses and that their use has been neglected in the analysis of survey data. We will consider three major applications: analyses to determine predictors and mechanisms of injury to children in passenger vehicle crashes using the population-based surveillance dataset of the Partners for Child Passenger Safety, to explore the development of cardiovascular risk factors in children using the National Health and Nutrition Examination Survey and to determine the prevalence of smoking and cancer screening behavior among adults using calibration estimators developed to combine data from the Behavioral Risk Factor Surveillance Survey and the National Health Interview Survey.

描述（由申请人提供）：拟议研究的目的是开发方法，用于通过开发基于模型的方法来分析具有高度差异化概率的数据，这些方法将设计方法的鲁棒性与可通过模型获得的最佳性结合在一起。在经常在国家基于人群的，复杂样本设计的健康调查中发现的选择样本设计不平等的样本设计中，选择的可能性与采样数据之间的相关性可能会引起偏见。重量等于选择概率的倒数的权重通常用于抵消这种偏见。高度不正确的样本设计具有较大的权重，可以在统计数据（例如人口平均估计值）中引入不必要的变异性。修剪重量可将大量重量减少到固定的切口值，并将重量调整到此值以下，以保持未修剪的权重总和。这会以引入一些偏见为代价降低可变性。标准方法不是“数据驱动的”：他们不使用数据来实现适当的偏见差异权衡，否则以高效的方式进行了。我们建议开发基于模型的方法，以补充标准的，基于临时设计的方法，以划分的概率设计，在这种概率上，由于样本重量引起的差异超过偏差校正。我们还将考虑使用这些模型来估计线性和广义线性回归模型中的总体参数。我们在分层和延伸后的已知概 - 探针样品设计的背景下开发这些模型，并将其扩展到更通用的多阶段群集样品设计中。我们计划开发贝叶斯模型，因为我们认为它们为传统的频繁分析提供了理论和实践优势，并且在调查数据的分析中忽略了它们的使用。我们将考虑三个主要应用：分析以确定乘用车中儿童伤害的预测因素和机制，使用基于人群的儿童乘客安全合作伙伴的基于人群的监视数据集，以探索儿童在国家健康和努力检查调查中使用国家健康和努力群体中的国家健康和果酱筛查的劳动估算估算的国家健康和努力均表现的儿童的心血管危险因素的发展健康面试调查。