Analysis of Linked Files: A Missing Data Perspective

TL;DR

This paper examines record linkage as a missing data problem, analyzing various statistical methods and their performance in handling linkage errors across different scenarios.

Contribution

It categorizes linkage analysis methods under missing data frameworks and evaluates their assumptions and limitations through simulations.

Findings

Likelihood and Bayesian methods have specific assumptions that affect their performance.

Imputation and weighting methods offer alternative approaches with different trade-offs.

Simulation results highlight the strengths and weaknesses of each method under various conditions.

Abstract

In many applications, researchers seek to identify overlapping entities across multiple data files. Record linkage algorithms facilitate this task, in the absence of unique identifiers. As these algorithms rely on semi-identifying information, they may miss records that represent the same entity, or incorrectly link records that do not represent the same entity. Analysis of linked files commonly ignores such linkage errors, resulting in biased, or overly precise estimates of the associations of interest. We view record linkage as a missing data problem, and delineate the linkage mechanisms that underpin analysis methods with linked files. Following the missing data literature, we group these methods under three categories: likelihood and Bayesian methods, imputation methods, and weighting methods. We summarize the assumptions and limitations of the methods, and evaluate their performance in a wide range of simulation scenarios.