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）：拟议研究的目的是通过开发基于模型的方法，将设计方法的稳健性与通过模型可获得的最优性相联合收割机，开发用于分析具有高度差异的纳入概率的数据的方法。在全国性的、基于人口的、复杂样本设计的健康调查中经常发现的不平等选择概率样本设计中，选择概率与抽样数据之间的相关性可能会导致偏倚。权重等于选择概率的倒数，通常用来抵消这种偏差。高度不成比例的样本设计具有较大的权重，这可能会在统计量（如总体均值估计值）中引入不必要的变异性。权重修剪将大权重减小到固定的切割点值，并将权重调整到低于此值以保持未修剪的权重总和。这以引入一些偏差为代价减少了可变性。标准方法不是“数据驱动的”：它们不使用数据来进行适当的偏差-方差权衡，或者以非常低效的方式进行。我们建议开发基于模型的方法“重量修剪”，以补充标准，特设的设计为基础的方法，在不成比例的概率纳入设计，由于样本重量的方差超过偏倚校正。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。我们开发这些模型的背景下，分层和后分层已知概率的样本设计，并扩展到更一般的多阶段聚类样本设计。我们计划开发贝叶斯模型，因为我们相信，他们提供了理论和实践的优势，传统的频率分析，他们的使用被忽视的调查数据的分析。我们将考虑三个主要应用：使用儿童乘客安全合作伙伴基于人口的监测数据集进行分析，以确定客车碰撞中儿童受伤的预测因素和机制，使用国家健康和营养检查调查探索儿童心血管危险因素的发展，并使用校准估计值确定成人吸烟和癌症筛查行为的流行率联合收割机结合了行为危险因素监测调查和国家健康访谈调查的数据。