Improving the representativeness of American Indian Tribal Behavioral Risk Factor Surveillance System (TBRFSS) by machine learning and propensity score based data integration approach A1

通过机器学习和基于倾向评分的数据集成方法提高美洲印第安人部落行为风险因素监测系统（TBRFSS）的代表性A1

• 批准号: 10063407
• 负责人: Sixia Chen
• 金额: $ 11.52万
• 依托单位: UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-26 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063407
• 关键词: Adult; Age; American; American Indians; Behavioral; Behavioral Risk Factor Surveillance System; Calibration; Censuses; Code; Communities; Community Surveys; Cross-Sectional Studies; Custom; Data; Data Sources; Diabetes Mellitus; Disease; Epidemiology; Ethnic group; Event; Failure; Funding; Future; General Population; Geographic state; Goals; Health; Health Fairs; Health Surveys; Health behavior; High Prevalence; Kansas; Machine Learning; Methodology; Methods; Modeling; Not Hispanic or Latino; Oklahoma; Performance; Population; Population Analysis; Population Study; Prevalence; Probability; Probability Samples; Publishing; Race; Research; Research Personnel; Respondent; Risk Factors; Sample Size; Sampling; Smoking; Surveys; Target Populations; Testing; Texas; Time; Tobacco; Training; United States Indian Health Service; Weight; Work; Youth; base; behavioral study; cigarette smoking; cluster computing; cost; data integration; data quality; design; experience; improved; individualized prevention; innovation; insight; multidisciplinary; novel; personalized medicine; population health; simulation; smoking prevalence; success; therapy development; tribal health

PROJECT SUMMARY Previous studies showed discrepancies of health and behavior prevalence between American Indians (AI) population and other racial or ethnic groups. Most health surveys have certain limitations when studying AI population due to the small sample sizes for AI population. Data collected by AI Tribal Epidemiology Centers (TECs) provides an excellent opportunity to conduct research for AI population due to sufficient sample size and extensive information. However, most surveys conducted by TECs used non-probability sampling design (e.g. convenient sample) due to its lower cost and increased time efficiency. Non-probability sample may suffer from sampling, coverage and nonresponse errors without further proper adjustments. Such difficulties greatly hampers the analysis of AI population in health and behavior research. Our general hypothesis is that data integration by combining information from non-probability and probability samples can reduce sampling, coverage and nonresponse errors in original non-probability sample. The Goal of this project is to develop an accurate and robust data integration methodology for AI population analysis specifically tailored to health and behavior research. During the past years, we have 1) studied data integration using calibration and parametric modeling approaches; 2) investigated machine learning and propensity score modeling methods in survey sampling and other fields; and 3) assembled an experienced team of multi-disciplinary team of experts. In this project, we propose to capitalize on our expertise and fulfill the following Specific Aims: Aim 1. Develop a data integration approach using machine learning and propensity score modeling We will develop machine learning and propensity score based data integration approaches to combine information from non-probability and probability samples. Compared to existing methods (i.e., Calibration, Parametric approach), our proposed approaches are more robust against the failure of underlying model assumptions. The inference is more general and multi-purpose (e.g. one can estimate most parameters such as means, totals and percentiles). Simulation studies will be performed to compare our proposed methods with other existing methods. A computing package will be built to implement the method in other settings. Aim 2. Evaluate the accuracy and robustness of the proposed method in AI health and behavior research We will use real data to validate the proposed methods in terms of accuracy and robustness to the various data types. The performance will also be assessed by comparing with results from existing data integration methods such as calibration and parametric modeling approaches. The planned study takes advantage of a unique data source and expands the impact of the Indian Health Service (IHS)-funded research. We expect this novel integration method will vertically advance the field by facilitating the analysis based on non-probability sample, which can provide in-depth understanding regarding the AI population health and behavior studies.

项目摘要 以往的研究表明，美国印第安人（AI）之间的健康和行为流行的差异 人口和其他种族或族裔群体。大多数健康调查在研究AI时都有一定的局限性 由于人工智能人群的样本量较小，人工智能部落流行病学中心收集的数据 （TEC）提供了一个很好的机会，进行研究的人工智能人口，由于足够的样本量， 广泛的信息。然而，大多数由TEC进行的调查使用非概率抽样设计（例如， 方便的样品），这是由于其较低的成本和增加的时间效率。非概率样本可能遭受 抽样、覆盖率和无应答误差，而不作进一步适当调整。这种困难极大 阻碍了健康和行为研究中对AI人群的分析。 我们的一般假设是，数据整合通过结合信息从非概率和概率 样本可以减少原非概率样本中的抽样误差、覆盖误差和无回答误差。目标 该项目的目的是为人工智能人口分析开发一种准确而强大的数据集成方法 专门为健康和行为研究量身定制。 在过去的几年里，我们1）研究了使用校准和参数建模的数据集成 方法; 2）调查抽样中的机器学习和倾向评分建模方法， 三是组建了一支经验丰富的多学科专家队伍。 在这个项目中，我们建议利用我们的专业知识，实现以下具体目标： 目标1。使用机器学习和倾向评分建模开发数据集成方法 我们将开发基于机器学习和倾向评分的数据集成方法，以将联合收割机 非概率和概率样本的信息。与现有方法相比（即，校准， 参数方法），我们提出的方法是更强大的对失败的基础模型 假设。推断是更一般和多用途的（例如，可以估计大多数参数，例如 平均数、总数和单位数）。模拟研究将进行比较，我们提出的方法与 现有的其他方法。将建立一个计算包，以在其他环境中实施该方法。 目标2.评估所提出的方法在人工智能健康和行为研究中的准确性和鲁棒性 我们将使用真实的数据来验证所提出的方法的准确性和鲁棒性的各种数据 类型还将通过与现有数据集成方法的结果进行比较来评估性能 例如校准和参数建模方法。这项计划中的研究利用了一个独特的数据， 来源和扩大印度卫生服务（IHS）资助的研究的影响。我们期待这部小说 集成方法将通过方便基于非概率样本的分析纵向推进该领域， 这可以提供关于AI人群健康和行为研究的深入了解。