A Flexible Model for Record Linkage

TL;DR

This paper introduces FlexRL, a flexible, scalable model for record linkage that balances accuracy and computational efficiency, effectively handling registration errors and changing identifiers in large datasets.

Contribution

The paper presents a novel stochastic EM-based approach for record linkage that adapts to data complexity and improves upon existing methods in accuracy and flexibility.

Findings

FlexRL effectively links large datasets with high accuracy.

The model demonstrates robustness to variable quality in linking variables.

Open source R package implementation is available.

Abstract

Combining data from various sources empowers researchers to explore innovative questions, for example those raised by conducting healthcare monitoring studies. However, the lack of a unique identifier often poses challenges. Record linkage procedures determine whether pairs of observations collected on different occasions belong to the same individual using partially identifying variables (e.g. birth year, postal code). Existing methodologies typically involve a compromise between computational efficiency and accuracy. Traditional approaches simplify this task by condensing information, yet they neglect dependencies among linkage decisions and disregard the one-to-one relationship required to establish coherent links. Modern approaches offer a comprehensive representation of the data generation process, at the expense of computational overhead and reduced flexibility. We propose a flexible method, that adapts to varying data complexities, addressing registration errors and accommodating changes of the identifying information over time. Our approach balances accuracy and scalability, estimating the linkage using a Stochastic Expectation Maximisation algorithm on a latent variable model. We illustrate the ability of our methodology to connect observations using large real data applications and demonstrate the robustness of our model to the linking variables quality in a simulation study. The proposed algorithm FlexRL is implemented and available in an open source R package.