Linked data are increasingly being used for epidemiological research, to enhance primary research, and in planning, monitoring and evaluating public policy and services. Linkage error (missed links between records that relate to the same person or false links between unrelated records) can manifest in many ways: as missing data, measurement error and misclassification, unrepresentative sampling, or as a special combination of these that is specific to analysis of linked data: the merging and splitting of people that can occur when two hospital admission records are counted as one person admitted twice if linked and two people admitted once if not. Through these mechanisms, linkage error can ultimately lead to information bias and selection bias; so identifying relevant mechanisms is key in quantitative bias analysis. In this article we introduce five key concepts and a study classification system for identifying which mechanisms are relevant to any given analysis. We provide examples and discuss options for estimating parameters for bias analysis. This conceptual framework provides the 'links' between linkage error, information bias and selection bias, and lays the groundwork for quantitative bias analysis for linkage error.

中文翻译：

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对现代方法的思考：链接错误偏差。

链接数据越来越多地用于流行病学研究，增强基础研究以及规划，监视和评估公共政策和服务。链接错误（与同一人相关的记录之间缺少链接，或者不相关的记录之间存在错误的链接）可以通过多种方式表现出来：丢失数据，测量错误和分类错误，代表性不佳或作为分析所特有的特殊组合链接数据：当两个医院的入院记录被链接为一个人两次时被录入，如果两个人未链接一次时被录入一次，则可能发生人的合并和分裂。通过这些机制，链接错误最终会导致信息偏差和选择偏差。因此，确定相关机制是定量偏差分析的关键。在本文中，我们介绍了五个关键概念和一个研究分类系统，用于识别哪些机制与任何给定分析相关。我们提供示例并讨论用于估计偏差分析参数的选项。这个概念框架提供了链接错误，信息偏向和选择偏向之间的“联系”，为链接错误的定量偏向分析奠定了基础。