Journal article
Evaluating the harmonisation potential of diverse cohort datasets
- Abstract:
- Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Supplementary materials, zip, 58.0KB, Terms of use)
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(Preview, Version of record, pdf, 476.8KB, Terms of use)
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- Publisher copy:
- 10.1007/s10654-023-00997-3
Authors
+ Medical Research Council
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- Funder identifier:
- https://ror.org/03x94j517
- Grant:
- MR/T033371/1
- MR/L01632X/1
- Publisher:
- Springer
- Journal:
- European Journal of Epidemiology More from this journal
- Volume:
- 38
- Issue:
- 6
- Pages:
- 605-615
- Place of publication:
- Netherlands
- Publication date:
- 2023-04-26
- Acceptance date:
- 2023-03-22
- DOI:
- EISSN:
-
1573-7284
- ISSN:
-
0393-2990
- Pmid:
-
37099244
- Language:
-
English
- Keywords:
- Pubs id:
-
1339331
- Local pid:
-
pubs:1339331
- Deposit date:
-
2025-06-25
- ARK identifier:
Terms of use
- Copyright holder:
- Bauermeister et al.
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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