DARSaW: Developing, Assessing, and Refining Synthetic Sampling Weights to Improve Generalizability of the All of Us Research Program Data

DARSaW：开发、评估和细化合成采样权重，以提高我们所有人研究计划数据的普遍性

• 批准号: 10796237
• 负责人: Qingxia Chen
• 金额: $ 22.45万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-17 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10796237
• 关键词: Affect; All of Us Research Program; American; Baseline Surveys; Biomedical Research; Calibration; Case Study; Censuses; Cohort Studies; Collaborations; Community Surveys; Compensation; Complex; Data; Data Set; Disclosure; Disease; Disparity; Effectiveness; Ethnic Origin; Gender; Geographic Locations; Geography; Goals; Housing; Hypertension; Individual; Literature; Longitudinal cohort; Masks; Methodology; Methods; National Health and Nutrition Examination Survey; Obesity; Participant; Phenotype; Population; Prevalence; Probability; Publishing; Race; Research Personnel; Risk; Sample Size; Sampling; Statistical Methods; Surveys; Target Populations; Testing; Underrepresented Populations; United States; Weight; Work; cohort; data resource; design; disability; effectiveness evaluation; improved; machine learning method; multimodal data; recruit; response; statistical learning; statistics

Project Summary The All of Us Research Program (All of Us) is a large-scale initiative to collect and study multimodal data from over one million participants living in the United States (U.S.). Studies have shown significant disparities in disease prevalence compared to the broader U.S. population, potentially due to the overrepresentation of traditionally underrepresented groups. The challenge that limits the representativeness of All of Us to the target U.S. population is that the data are collected through a non-probabilistic sample design. This proposal aims to leverage two types of external data resources from the U.S. population to construct reliable Synthetic sampling Weights (SaW) for All of Us to mimic a probabilistic sample design and improve generalizability. The first external data resource, National Health and Nutrition Examination Survey (NHANES), creates a nationally representative dataset with validated sampling weights and individual-level data made publicly available. However, NHANES’ sample size is relatively small and can result in under-coverage. The second external data resource, the U.S. Census and the American Community Survey (ACS), are large-scale nationwide surveys that provide more but aggregated demographic and housing information about the U.S. population, compensating for the limitation of NHANES. However, individual-level data are not available. Utilizing the external data resources available in NHANES, the U.S. Census, and ACS, this project will develop, assess, and refine Synthetic sampling Weights (DARSaW) to improve the generalizability of All of Us to the target U.S. population. In Aim 1, we will develop the SaW for All of Us by leveraging the individual-level data from the NHANES and rich but aggregated summary statistics from the U.S. Census and the American Community Survey. In Aim 2, the effectiveness of the SaW will be assessed through case studies, comparing unweighted and SaW-weighted estimates of obesity, hypertension, and disability. We will iterate between Aims 1 and 2 to refine SaWs at the presence of discrepancy by post-calibrating to broader and deeper aggregated statistics from the target population. The goal of this proposal is to demonstrate the ability of the SaW to improve the generalizability of the All of Us data, enabling researchers to draw valid conclusions about the target U.S. population.

项目摘要 我们所有人研究计划(我们所有人)是一个大规模的收集和研究 来自100多万居住在美国(美国)的参与者的多模式数据。研究 与更广泛的美国相比，在疾病患病率方面显示出显著的差异。 这可能是由于传统上代表性不足的群体人数过多所致。 将我们所有人的代表性限制在目标美国人口上的挑战是 数据是通过非概率样本设计收集的。这项提议旨在利用 从美国人口两种类型的外部数据资源构建可靠的综合 抽样权重(SAW)用于我们所有人模仿概率样本设计和改进 概括性。第一个外部数据来源--国民健康与营养检查 调查(NHANES)，创建具有验证抽样权重的全国代表性数据集 以及公开的个人层面的数据。然而，NHANES的样本量相对较少 规模较小，可能导致覆盖范围不足。第二个外部数据来源，美国人口普查 和美国社区调查(ACS)，都是大规模的全国性调查，提供 更多但汇总了有关美国人口的人口和住房信息， 弥补NHANES的局限性。然而，个人层面的数据不可用。 利用NHANES、美国人口普查和ACS中可用的外部数据资源，这 该项目将开发、评估和改进合成抽样权重(DARSaW)，以改进 我们所有人对目标美国人口的概括性。在目标1中，我们将开发用于 我们所有人都通过利用来自NHANE和丰富但聚合的个人级别的数据 来自美国人口普查和美国社区调查的汇总统计数据。在目标2中， 将通过案例研究评估SAW的有效性，比较未加权和 对肥胖、高血压和残疾的SAW加权估计。我们将在目标1之间迭代 2在存在差异的情况下，通过后校准到更宽和更深来精炼锯子 来自目标人群的汇总统计数据。这项提议的目标是证明 SAW提高我们所有人数据的概括性的能力，使研究人员能够 得出有关目标美国人口的有效结论。