A simple sampling method for estimating the accuracy of large scale record linkage projects
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 t...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
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Schattauer Publishers
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/26908 |
| _version_ | 1848752118352052224 |
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| author | Boyd, James Guiver, T. Randall, Sean Ferrante, Anna Semmens, James Anderson, P. Dickinson, T. |
| author_facet | Boyd, James Guiver, T. Randall, Sean Ferrante, Anna Semmens, James Anderson, P. Dickinson, T. |
| author_sort | Boyd, James |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:03:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-26908 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:03:32Z |
| publishDate | 2016 |
| publisher | Schattauer Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-269082019-02-19T05:35:40Z A simple sampling method for estimating the accuracy of large scale record linkage projects Boyd, James Guiver, T. Randall, Sean Ferrante, Anna Semmens, James Anderson, P. Dickinson, T. 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. 2016 Journal Article http://hdl.handle.net/20.500.11937/26908 10.3414/ME15-01-0152 Schattauer Publishers fulltext |
| spellingShingle | Boyd, James Guiver, T. Randall, Sean Ferrante, Anna Semmens, James Anderson, P. Dickinson, T. A simple sampling method for estimating the accuracy of large scale record linkage projects |
| title | A simple sampling method for estimating the accuracy of large scale record linkage projects |
| title_full | A simple sampling method for estimating the accuracy of large scale record linkage projects |
| title_fullStr | A simple sampling method for estimating the accuracy of large scale record linkage projects |
| title_full_unstemmed | A simple sampling method for estimating the accuracy of large scale record linkage projects |
| title_short | A simple sampling method for estimating the accuracy of large scale record linkage projects |
| title_sort | simple sampling method for estimating the accuracy of large scale record linkage projects |
| url | http://hdl.handle.net/20.500.11937/26908 |