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

项目摘要 我们所有人的研究计划（我们所有人）是一个大规模的倡议，收集和研究 多模态数据来自生活在美国的100多万参与者。研究 与美国其他地区相比， 这可能是由于传统上代表性不足的群体人数过多。 限制我们所有人对美国目标人口的代表性的挑战是， 通过非概率抽样设计收集数据。该提案旨在利用 两种类型的外部数据资源从美国人口构建可靠的合成 我们所有人的抽样权重（SaW），以模仿概率样本设计并改进 普遍性第一个外部数据源，国家健康和营养检查 调查（NHANES），创建具有验证抽样权重的全国代表性数据集 以及个人层面的数据。然而，NHANES的样本量相对较小， 小，可能导致覆盖不足。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， 更多但汇总的关于美国人口的人口统计和住房信息， 弥补了NHANES的局限性。然而，没有个人层面的数据。 利用NHANES、美国人口普查和ACS中可用的外部数据资源， 该项目将开发、评估和完善综合抽样权重（DARSaW），以提高 我们所有人对美国目标人群的普遍性。在目标1中，我们将开发用于 我们所有人通过利用来自NHANES的个人层面的数据和丰富但汇总的数据 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， SAW的有效性将通过案例研究进行评估，比较未加权和 肥胖、高血压和残疾的SAW加权估计值。我们将在目标1 和2通过后校准以更宽和更深来在存在差异时细化SAW 目标人群的综合统计数据。本提案的目的是展示 SaW能够提高All of Us数据的普遍性，使研究人员能够 对目标美国人口得出有效结论。

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.