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 我们所有的研究计划（我们所有人）都是一项大规模倡议，用于收集和学习 来自美国（美国）的一百万参与者的多模式数据（美国）。研究 与更广泛的美国相比，疾病患病率的差异很大 人口，可能是由于传统代表性不足的群体过多的代表。 限制我们所有人对目标美国人口的代表的挑战是 数据是通过非稳定样本设计收集的。该建议旨在利用 美国人群的两种类型的外部数据资源来构建可靠的合成 对我们所有人进行采样权重（SAW），以模仿概率样本设计并改进 概括性。第一个外部数据资源，国家健康和营养检查 调查（NHANES），创建一个具有经过验证权重的全国代表性数据集 和个人级别的数据公开可用。但是，NHANES的样本量相对 小，可能导致覆盖不足。第二个外部数据资源，美国人口普查 美国社区调查（ACS）是提供全国性调查的大规模调查 更多但汇总的人口和住房信息，有关美国人口的信息， 补偿NHANES的限制。但是，个人级别的数据不可用。 利用NHANES，美国人口普查和ACS可用的外部数据资源，这 项目将开发，评估和完善合成抽样权重（DARSAW），以改善 我们所有人对目标美国人群的概括性。在AIM 1中，我们将开发锯 我们所有人都通过利用NHANES的个人级别数据和丰富的数据来汇总 美国人口普查和美国社区调查的摘要统计数据。在AIM 2中 锯的有效性将通过案例研究评估，比较未加权和 对肥胖，高血压和残疾的锯估计估计值。我们将在目标1之间迭代1 和2通过降低后校准到越来越深的距 来自目标人群的总统计数据。该提议的目的是证明 锯能​​够提高我们所有数据的普遍性的能力，使研究人员能够 得出有关目标美国人群的有效结论。