  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.
