  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.
