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Advancing Record Linkage Research: Optimal Linkage Decisions and Propagating Linkage Uncertainty

推进记录关联研究：最优关联决策和传播关联不确定性

基本信息

批准号：
1852841
负责人：
Mauricio Sadinle Garcia-Ruiz
金额：
$ 15万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852841&HistoricalAwards=false
关键词：
Advancing Record Linkage Research Optimal

项目摘要

This project will advance research on record linkage. It is increasingly common to find complementary information on individuals scattered across multiple data sources. To take full advantage of these data sources, researchers need to be able to link information on the same individuals. In many applications, however, there are no unique identifiers of the individuals in the datafiles. This makes it difficult to recognize which records correspond to the same individuals. Statistical methodology will be developed for creating merged datafiles and for improving analyses of the linked data. These data linkages will allow richer data analyses and potentially substitute for or facilitate new data collection efforts. Researchers across disciplines will benefit from being able to use statistically rigorous procedures to merge datasets and to carry out analyses with linked data. The ability to create and analyze richer datasets will facilitate understanding of policy options in important areas such as education and health, thus furthering societal interests. A graduate student will be trained as part of this project, and the techniques will be made available as part of free software packages along with tutorials.This project will use the output of probabilistic record linkage procedures to develop rigorous statistical methodology for creating merged datafiles. Coherent approaches for propagating linkage uncertainty into subsequent analyses will be explored. To create merged datasets, the investigator will derive an estimator of the true linkage of the datafiles. A loss function will be developed through which researchers will be able to give different weights to different types of linkage errors. The linkage estimator will be derived by minimizing the expected value of the researcher-defined loss function. The point estimators also will include the option of "abstaining" from linking records for which the correct links are highly uncertain. To perform statistical analyses with merged data, the investigator will explore procedures in which researchers carry out the statistical analysis they are interested in for each of several plausible linkages of the data and then combine the output from these analyses. The procedures will be validated theoretically, via simulation studies, and using real data analyses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将推进记录链接的研究。寻找分散在多个数据源中的个人的补充信息变得越来越普遍。为了充分利用这些数据源，研究人员需要能够链接同一个人的信息。然而，在许多应用中，数据文件中没有个人的唯一标识符。这使得很难识别哪些记录对应于同一个人。将开发统计方法来创建合并数据文件并改进链接数据的分析。这些数据链接将允许更丰富的数据分析，并有可能替代或促进新的数据收集工作。跨学科的研究人员将受益于能够使用严格的统计程序来合并数据集并使用链接数据进行分析。创建和分析更丰富的数据集的能力将有助于理解教育和健康等重要领域的政策选择，从而促进社会利益。作为该项目的一部分，将培训一名研究生，并且这些技术将作为免费软件包的一部分以及教程提供。该项目将使用概率记录链接程序的输出来开发用于创建合并数据文件的严格统计方法。将探索将连锁不确定性传播到后续分析中的连贯方法。为了创建合并的数据集，研究人员将得出数据文件真实链接的估计量。将开发一个损失函数，通过该函数，研究人员将能够为不同类型的链接错误赋予不同的权重。链接估计器将通过最小化研究人员定义的损失函数的期望值来导出。点估计器还将包括“放弃”链接记录的选项，这些记录的正确链接高度不确定。为了使用合并数据进行统计分析，研究人员将探索研究人员对数据的几个看似合理的联系中的每一个进行他们感兴趣的统计分析的程序，然后合并这些分析的输出。该程序将通过模拟研究和真实数据分析从理论上进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。