Data linkage is increasingly being used to combine data from different sources with the aim of identifying and bringing together records from separate files, which correspond to the same entities. Usually, data linkage is not a trivial procedure and linkage errors, false and missed links, are unavoidable. In these cases, standard statistical techniques may produce misleading inference. In this paper, we propose a method for secondary linear regression analysis, where the linked data have to be prepared by someone else, and neither the match‐key variables nor the unlinked records are available to the analyst. We develop also a diagnostic test for the assumption of non‐informative linkage errors, which is required for all existing secondary analysis adjustment methods. Our approach provides important advantages: it relies on the realistic assumption that the probabilities of correct linkage vary across the records but it does not assume that one is able to estimate the probability of correct linkage for each individual record. Moreover, it accommodates in a simple manner the general situation where the files are of different sizes and none of them is a subset of another. The proposed methodology of adjustment and testing is studied by simulation and applied to real data.

中文翻译：

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链接数据线性回归

越来越多地使用数据链接来组合来自不同来源的数据，以识别和汇集来自对应于相同实体的单独文件中的记录。通常，数据链接不是一个简单的过程，并且不可避免会发生链接错误，错误和丢失的链接。在这些情况下，标准的统计技术可能会产生误导性的推断。在本文中，我们提出了一种用于二次线性回归分析的方法，其中链接数据必须由其他人准备，分析人员既没有匹配键变量也没有未链接记录。我们还针对非信息性链接错误的假设开发了诊断测试，这是所有现有的二级分析调整方法所必需的。我们的方法具有重要的优势：它基于现实的假设，即正确链接的概率在整个记录中有所不同，但它并不假设人们能够估计每个单独记录的正确链接的概率。此外，它以一种简单的方式适应了文件大小不同且文件都不是另一个子集的一般情况。通过仿真研究提出的调整和测试方法，并将其应用于实际数据。