RAILS：面向志愿者型国家生物样本库的合成抽样权重——基于“All of Us 研究计划”的案例研究
RAILS: A Synthetic Sampling Weights for Volunteer-Based National Biobanks -- A Case Study with the All of Us Research Program
AI总结：
针对国家生物样本库非概率抽样导致的代表性不足问题，提出结合伪设计模型与后进先出准则的RAILS合成权重法，经模拟与All of Us项目验证可有效降偏差、提效率，助力人群表型基因型患病率估计。
摘要：
国家生物样本库是推动医学研究的核心资源，但其非概率抽样设计限制了对目标人群的代表性。本文提出一种利用高质量全国性调查为非概率队列研究构建合成抽样权重的方法，旨在提升代表性。具体而言，我们聚焦于推导更精准的基础权重（通过满足总体约束增强校准效果），以及自动化实现由数据支撑的校准交叉表选择。该方法将基于伪设计的模型与全新的后进先出准则相结合，提升了估计的准确性与稳定性。大量模拟实验表明，我们提出的RAILS方法与现有方法相比，可降低偏差、提升效率并增强推断效力。我们将该方法应用于All of Us Research Program，采用2020年全国健康访谈调查与2022年美国社区调查的数据，将常见表型的患病率估计值与全国基准进行对比。结果凸显了本方法有效降低非概率样本选择偏差的能力，为提升生物样本库代表性提供了有价值的工具。利用为All of Us Research Program开发的抽样权重，我们能够估计美国人群中未被全国概率研究覆盖的表型与基因型的患病率。
英文摘要：
While national biobanks are essential for advancing medical research, their non-probability sampling designs limit their representativeness of the target population. This paper proposes a method that leverages high-quality national surveys to create synthetic sampling weights for non-probabilistic cohort studies, aiming to improve representativeness. Specifically, we focus on deriving more accurate base weights, which enhance calibration by meeting population constraints, and on automating data-supported selection of cross-tabulations for calibration. This approach combines a pseudo-design-based model with a novel Last-In-First-Out criterion, enhancing the accuracy and stability of the estimates. Extensive simulations demonstrate that our method, named RAILS, reduces bias, improves efficiency, and strengthens inference compared to existing approaches. We apply the proposed method to the All of Us Research Program, using data from the National Health Interview Survey 2020 and the American Community Survey 2022 and comparing prevalence estimates for common phenotypes against national benchmarks. The results underscore our method's ability to effectively reduce selection bias in non-probability samples, offering a valuable tool for enhancing biobank representativeness. Using the developed sampling weights for the All of Us Research Program, we can estimate the prevalence of the United States population for phenotypes and genotypes not captured by national probability studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
