A simple sampling method for estimating the accuracy of large scale record linkage projects
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This article is not an exact copy of the original published article in Methods of Information in Medicine. The definitive publisher-authenticated version of Boyd, J. and Guiver, T. and Randall, S. and Ferrante, A. and Semmens, J. and Anderson, P. and Dickinson, T. 2016. A simple sampling method for estimating the accuracy of large scale record linkage projects. Methods of Information in Medicine. 55 (3): pp. 276-283. is available online at: http://doi.org/10.3414/ME15-01-0152
Background: Record linkage techniques allow different data collections to be brought together to provide a wider picture of the health status of individuals. Ensuring high linkage quality is important to guarantee the quality and integrity of research. Current methods for measuring linkage quality typically focus on precision (the proportion of incorrect links), given the difficulty of measuring the proportion of false negatives. Objectives: The aim of this work is to introduce and evaluate a sampling based method to estimate both precision and recall following record linkage. Methods: In the sampling based method, record-pairs from each threshold (including those below the identified cut-off for acceptance) are sampled and clerically reviewed. These results are then applied to the entire set of record-pairs, providing estimates of false positives and false negatives. This method was evaluated on a synthetically generated dataset, where the true match status (which records belonged to the same person) was known. Results: The sampled estimates of linkage quality were relatively close to actual linkage quality metrics calculated for the whole synthetic dataset. The precision and recall measures for seven reviewers were very consistent with little variation in the clerical assessment results (overall agreement using the Fleiss Kappa statistics was 0.601). Conclusions: This method presents as a possible means of accurately estimating matching quality and refining linkages in population level linkage studies. The sampling approach is especially important for large project linkages where the number of record pairs produced may be very large often running into millions.
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Brown, A.; Randall, Sean; Ferrante, A.; Semmens, J.; Boyd, J. (2017)Background: Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. ...
Randall, Sean; Brown, Adrian; Boyd, James; Schnell, R.; Borgs, C.; Ferrante, Anna (2018)© 2018 The Author(s). Background: Record linkage is an important tool for epidemiologists and health planners. Record linkage studies will generally contain some level of residual record linkage error, where individual ...
Randall, Sean; Ferrante, Anna; Boyd, James; Semmens, James (2013)Background: Within the field of record linkage, numerous data cleaning and standardisation techniques are employed to ensure the highest quality of links. While these facilities are common in record linkage software ...