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

该项目将推进记录连接的研究。在多个数据源中找到关于个人的补充信息越来越普遍。为了充分利用这些数据源，研究人员需要能够将同一个人的信息联系起来。然而，在许多应用中，文件夹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相同的个人。将开发统计方法来创建合并的数据文件并改进对链接数据的分析。 这些数据连接将使数据分析更加丰富，并有可能取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统计上严格的程序来合并数据集，并对关联数据进行分析。创建和分析更丰富的数据集的能力将有助于理解教育和卫生等重要领域的政策选择，从而促进社会利益。一名研究生将作为本项目的一部分接受培训，这些技术将作为免费软件包的一部分沿着教程。本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的文档的严格统计方法。将探讨将连锁不确定性传播到后续分析的连贯方法。为了创建合并的数据集，研究者将推导出这些序列的真实关联的估计值。将开发一个损失函数，通过该函数，研究人员将能够对不同类型的连锁错误给予不同的权重。将通过最小化研究者定义的损失函数的期望值来导出关联估计量。点估计器还将包括“放弃”链接记录的选项，其中正确的链接是高度不确定的。为了对合并数据进行统计分析，研究者将探索研究人员对数据的几个合理联系中的每一个进行统计分析的程序，然后将这些分析的输出联合收割机组合起来。该程序将通过模拟研究和使用真实的数据分析进行理论验证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.