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

项目总结 以前的研究表明，美国印第安人(AI)之间的健康和行为流行率存在差异 人口和其他种族或民族群体。大多数健康调查在研究人工智能时都有一定的局限性 由于人工智能种群的样本量较小，因此需要对人工智能种群进行分类。AI部落流行病学中心收集的数据 (TECS)提供了一个对人工智能种群进行研究的绝佳机会，因为有足够的样本量和 广泛的信息。然而，TEC进行的大多数调查都采用了非概率抽样设计(例如 方便的样品)，因为它的成本更低，时间效率更高。非概率样本可能会受到 在没有进一步适当调整的情况下，抽样、覆盖和无响应错误。这样的困难很大 阻碍了健康和行为研究中对人工智能群体的分析。 我们的一般假设是，通过组合来自非概率和概率的信息来进行数据集成 样本可以减少原始非概率样本的抽样、覆盖和无响应误差。目标是 该项目的主要目的是为人工智能种群分析开发一种准确和健壮的数据集成方法 专门为健康和行为研究量身定做。 在过去的几年里，我们研究了使用校准和参数建模的数据集成 研究了抽样调查中的机器学习方法和倾向得分建模方法。 3)组建了一支经验丰富的多学科专家团队。 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.开发一种使用机器学习和倾向得分建模的数据集成方法 我们将开发基于机器学习和倾向评分的数据集成方法，以结合 来自非概率样本和概率样本的信息。与现有方法相比(即，校准， 参数方法)，我们提出的方法对底层模型的失效具有更强的鲁棒性 假设。推论更具一般性和多功能性(例如，可以估计大多数参数，如 平均值、总数和百分位数)。我们将进行模拟研究，将我们提出的方法与 其他现有的方法。将建立一个计算包来在其他环境中实施该方法。 目的2.评估提出的方法在人工智能健康和行为研究中的准确性和稳健性 我们将使用实际数据来验证所提出的方法的准确性和对各种数据的稳健性 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独特的数据 获取并扩大印度卫生服务(IHS)资助的研究的影响。我们期待着这本小说 积分方法将通过促进基于非概率样本的分析来垂直推进该领域， 这可以为深入了解人工智能人群的健康和行为研究提供参考。

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.