arXiv subjects

Kayané Robach

Publications and source records attributed to Kayané Robach.

4 recordsLinked to original sources

arXiv2026

FlexRL: an R package for Flexible Record Linkage and Linked Data Quality Assessment

FlexRL is an R package for probabilistic record linkage and inference on linked data. It provides a method to link observations from the same entities across two data sources that lack a unique identifier but share partially identifying variables. This method fits a hierarchical latent variable model of the data generation process, rather than classifying pairs of records from the comparison of their values, and accommodates registration errors and variables changing over time. Since linkage may affect subsequent analyses, the package also provides tools to assess the linked data quality as functions of the linkage decision rule. Two false discovery proportion estimators are implemented: a probabilistic approach contingent on the record linkage model and an augmented approach relying on an auxiliary estimation using synthetic data. Several measures of divergence of the linked sample from the source population are provided. These tools are agnostic to the underlying record linkage model and can be applied to any method. Together, they connect the output of the linkage to the validity of the subsequent analyses. The implementation combines standard R functions and S3 methods with Rcpp computations, resulting in fast execution times and a low memory footprint.

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arXiv2026

Causal Record Linkage

Combining observations from multiple data sources creates a valuable resource for research. In causal inference, record linkage methods grant researchers access to the covariates needed for unbiased estimation and offer the means to explore (long-term) outcomes. This paper examines how record linkage conflicts with the assumptions required for identifying causal effects. Our investigation reveals that linkage errors primarily threaten consistency in the causal framework, leading to attenuation bias in the inference due to discrepancies in the linked data. In attempting to address this bias by being more stringent on the linkage, sampling bias inadvertently emerges from a covariate shift between the baseline and the linked samples, as profiles with more distinctive information are more likely to be recovered. Despite these challenges, we identify settings in which record linkage and causal inference can be soundly combined, and show how the effect estimated on linked data can be generalised, with linkage decisions informed accordingly. We further identify when existing and novel solutions support valid causal inference on linked data, and when inference should instead be treated with caution or even abandoned. We illustrate the challenges raised and the potential solutions using real data from a study of women's health and a simulation study.

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arXiv2025

False Discovery estimation in Record Linkage

Integrating data from multiple sources expands research opportunities at low cost. However, due to different data collection processes and privacy constraints, unique identifiers are unavailable. Record Linkage (RL) algorithms address this by probabilistically linking records based on partially identifying variables. Since these variables lack the strength to perfectly combine information, RL procedures yield an imperfect set of linked records. Therefore, assessing the false discovery proportion (FDP) in RL is crucial for ensuring the reliability of subsequent analyses. In this paper, we introduce a novel method for estimating the FDP in RL for two overlapping data sets. We synthesise data from their estimated empirical distribution and use it along with real data in the linkage process. Since synthetic records cannot form links with real entities, they provide a means to estimate the amount of falsely linked pairs. Notably, this method applies to all RL techniques and across diverse settings where links and non-links have similar distributions -- typical in complex tasks with poorly discriminative linking variables and multiple records sharing similar information while representing different entities. By identifying the FDP in RL and selecting suitable model parameters, our approach enables to assess and improve the reliability of linked data. We evaluate its performance using established RL algorithms and benchmark data applications before deploying it to link siblings from the Netherlands Perinatal Registry, where the reliability of previous RL applications has never been confirmed. Through this application, we highlight the importance of accounting for linkage errors when studying mother-child dynamics in healthcare records.

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arXiv2025

A Flexible Model for Record Linkage

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.

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