标题： 在链接文件的二次回归分析中放松强非信息性连锁错误的假设 显示英文标题

标题： Relaxing the Assumption of Strongly Non-Informative Linkage Error in Secondary Regression Analysis of Linked Files

摘要： 对来自多个来源的记录进行链接后得到的文件的数据分析通常会受到链接错误的影响。 记录可能被错误地链接，或者它们的链接可能被遗漏。 因此，必须考虑这些错误以确保链接后的推断有效。 在此，我们提出了一种扩展方法，用于基于两组分混合模型的带有链接协变量和响应的回归的一般框架，该框架在先前的研究中已经开发出来。 该框架解决了二次分析的挑战性情况，其中仅可获得链接数据，且关于记录链接过程的信息有限。 本文所考虑的扩展放松了框架中的强非信息性链接假设，根据该假设，链接不依赖于分析中使用的协变量，这在实践中可能是有限制的。 通过模拟和一个案例研究来检验所提出的扩展的有效性。 显示英文摘要

摘要： Data analysis of files that are a result of linking records from multiple sources are often affected by linkage errors. Records may be linked incorrectly, or their links may be missed. In consequence, it is essential that such errors are taken into account to ensure valid post-linkage inference. Here, we propose an extension to a general framework for regression with linked covariates and responses based on a two-component mixture model, which was developed in prior work. This framework addresses the challenging case of secondary analysis in which only the linked data is available and information about the record linkage process is limited. The extension considered herein relaxes the assumption of strongly non-informative linkage in the framework according to which linkage does not depend on the covariates used in the analysis, which may be limiting in practice. The effectiveness of the proposed extension is investigated by simulations and a case study.