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GUILD: GUidance for Information about Linking Data sets†
Record linkage of administrative and survey data is increasingly used to generate evidence to inform policy and services. Although a powerful and efficient way of generating new information from existing data sets, errors related to data processing before, during and after linkage can bias results....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896589/ https://www.ncbi.nlm.nih.gov/pubmed/28369581 http://dx.doi.org/10.1093/pubmed/fdx037 |
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author | Gilbert, Ruth Lafferty, Rosemary Hagger-Johnson, Gareth Harron, Katie Zhang, Li-Chun Smith, Peter Dibben, Chris Goldstein, Harvey |
author_facet | Gilbert, Ruth Lafferty, Rosemary Hagger-Johnson, Gareth Harron, Katie Zhang, Li-Chun Smith, Peter Dibben, Chris Goldstein, Harvey |
author_sort | Gilbert, Ruth |
collection | PubMed |
description | Record linkage of administrative and survey data is increasingly used to generate evidence to inform policy and services. Although a powerful and efficient way of generating new information from existing data sets, errors related to data processing before, during and after linkage can bias results. However, researchers and users of linked data rarely have access to information that can be used to assess these biases or take them into account in analyses. As linked administrative data are increasingly used to provide evidence to guide policy and services, linkage error, which disproportionately affects disadvantaged groups, can undermine evidence for public health. We convened a group of researchers and experts from government data providers to develop guidance about the information that needs to be made available about the data linkage process, by data providers, data linkers, analysts and the researchers who write reports. The guidance goes beyond recommendations for information to be included in research reports. Our aim is to raise awareness of information that may be required at each step of the linkage pathway to improve the transparency, reproducibility, and accuracy of linkage processes, and the validity of analyses and interpretation of results. |
format | Online Article Text |
id | pubmed-5896589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58965892018-04-17 GUILD: GUidance for Information about Linking Data sets† Gilbert, Ruth Lafferty, Rosemary Hagger-Johnson, Gareth Harron, Katie Zhang, Li-Chun Smith, Peter Dibben, Chris Goldstein, Harvey J Public Health (Oxf) Perspectives Record linkage of administrative and survey data is increasingly used to generate evidence to inform policy and services. Although a powerful and efficient way of generating new information from existing data sets, errors related to data processing before, during and after linkage can bias results. However, researchers and users of linked data rarely have access to information that can be used to assess these biases or take them into account in analyses. As linked administrative data are increasingly used to provide evidence to guide policy and services, linkage error, which disproportionately affects disadvantaged groups, can undermine evidence for public health. We convened a group of researchers and experts from government data providers to develop guidance about the information that needs to be made available about the data linkage process, by data providers, data linkers, analysts and the researchers who write reports. The guidance goes beyond recommendations for information to be included in research reports. Our aim is to raise awareness of information that may be required at each step of the linkage pathway to improve the transparency, reproducibility, and accuracy of linkage processes, and the validity of analyses and interpretation of results. Oxford University Press 2018-03 2017-03-28 /pmc/articles/PMC5896589/ /pubmed/28369581 http://dx.doi.org/10.1093/pubmed/fdx037 Text en © The Author 2017. Published by Oxford University Press on behalf of Faculty of Public Health. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Perspectives Gilbert, Ruth Lafferty, Rosemary Hagger-Johnson, Gareth Harron, Katie Zhang, Li-Chun Smith, Peter Dibben, Chris Goldstein, Harvey GUILD: GUidance for Information about Linking Data sets† |
title | GUILD: GUidance for Information about Linking Data sets† |
title_full | GUILD: GUidance for Information about Linking Data sets† |
title_fullStr | GUILD: GUidance for Information about Linking Data sets† |
title_full_unstemmed | GUILD: GUidance for Information about Linking Data sets† |
title_short | GUILD: GUidance for Information about Linking Data sets† |
title_sort | guild: guidance for information about linking data sets† |
topic | Perspectives |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896589/ https://www.ncbi.nlm.nih.gov/pubmed/28369581 http://dx.doi.org/10.1093/pubmed/fdx037 |
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