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.

项目概要 我们所有人研究计划（All of Us）是一项大规模的倡议，旨在收集和研究 来自居住在美国 (U.S.) 的超过 100 万参与者的多模式数据。 与美国更广泛的地区相比，各地区的疾病患病率存在​​显着差异。 人口，可能是由于传统上代表性不足的群体代表性过高。 限制我们所有人对美国目标人群的代表性的挑战是 该提案旨在通过非概率样本设计收集数据。 来自美国人口的两类外部数据资源构建可靠的综合 为我们所有人提供采样权重 (SaW)，以模仿概率样本设计并改进 第一个外部数据资源，国家健康和营养检查。 调查 (NHANES)，创建具有经过验证的抽样权重的全国代表性数据集 然而，NHANES 的样本量相对较小。 第二个外部数据资源是美国人口普查。 和美国社区调查 (ACS) 是大规模的全国性调查，提供 更多关于美国人口的人口统计和住房信息， 弥补 NHANES 的局限性，但尚无个人层面的数据。 利用 NHANES、美国人口普查和 ACS 中可用的外部数据资源， 项目将开发、评估和完善综合抽样权重 (DARSaW)，以改进 我们所有人对美国目标人群的普遍性 在目标 1 中，我们将开发 SAW。 我们所有人都利用来自 NHANES 的个人级数据以及丰富但汇总的数据 在目标 2 中，美国人口普查和美国社区调查的汇总统计数据。 SAW 的有效性将通过案例研究进行评估，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 之间进行迭代。 2 在存在差异的情况下通过后校准到更广泛和更深的程度来改进 SAW 该提案的目标是展示目标人群的汇总统计数据。 SAW 提高“我们所有人”数据的普遍性的能力，使研究人员能够 对美国目标人群得出有效的结论。