A survey on UK researchers' views regarding their experiences with the de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets
- PMID: 39927449
- PMCID: PMC11809122
- DOI: 10.1177/17407745241259086
A survey on UK researchers' views regarding their experiences with the de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets
Abstract
Background: There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community. However, there is no standardised set of recommendations on how to anonymise and prepare clinical trial datasets for sharing, while an ever-increasing number of anonymised datasets are becoming available for secondary research. Our aim was to explore the current views and experiences of researchers in the United Kingdom about de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets.
Methods: We used an online exploratory cross-sectional descriptive survey that consisted of both open-ended and closed questions.
Results: We had 38 responses to invitation from June 2022 to October 2022. However, 35 participants (92%) used internal documentation and published guidance to de-identify/anonymise clinical trial datasets. De-identification, followed by anonymisation and then fulfilling data holders' requirements before access was granted (controlled access), was the most common process for releasing the datasets as reported by 18 (47%) participants. However, 11 participants (29%) had previous knowledge of re-identification risk estimation, but they did not use any of the methodologies. Experiences in the process of de-identifying/anonymising the datasets and maintaining such datasets were mostly negative, and the main reported issues were lack of resources, guidance, and training.
Conclusion: The majority of responders reported using documented processes for de-identification and anonymisation. However, our survey results clearly indicate that there are still gaps in the areas of guidance, resources and training to fulfil sharing requests of de-identified/anonymised datasets, and that re-identification risk estimation is an underdeveloped area.
Keywords: Clinical trials; data anonymisation; data sharing; de-identification; re-identification; re-identification risk.
Conflict of interest statement
Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Similar articles
-
Current recommendations/practices for anonymising data from clinical trials in order to make it available for sharing: A scoping review.Clin Trials. 2022 Aug;19(4):452-463. doi: 10.1177/17407745221087469. Epub 2022 Jun 22. Clin Trials. 2022. PMID: 35730910 Free PMC article.
-
Data sharing in clinical trials - practical guidance on anonymising trial datasets.Trials. 2018 Jan 10;19(1):25. doi: 10.1186/s13063-017-2382-9. Trials. 2018. PMID: 29321053 Free PMC article.
-
Sharing traumatic stress research data: assessing and reducing the risk of re-identification.Eur J Psychotraumatol. 2025 Dec;16(1):2499296. doi: 10.1080/20008066.2025.2499296. Epub 2025 May 19. Eur J Psychotraumatol. 2025. PMID: 40387730 Free PMC article. Review.
-
Protecting patient privacy when sharing patient-level data from clinical trials.BMC Med Res Methodol. 2016 Jul 8;16 Suppl 1(Suppl 1):77. doi: 10.1186/s12874-016-0169-4. BMC Med Res Methodol. 2016. PMID: 27410040 Free PMC article.
-
Preparing individual patient data from clinical trials for sharing: the GlaxoSmithKline approach.Pharm Stat. 2014 May-Jun;13(3):179-83. doi: 10.1002/pst.1615. Epub 2014 Mar 25. Pharm Stat. 2014. PMID: 24668938
References
-
- Dal-Ré R. Access to anonymized individual participant clinical trials data: a radical change of mind by the most prestigious medical journals. Arch Bronconeumol 2018; 54(2): 65–67. - PubMed
-
- Bertagnolli M, Sartor O, Chabner B, et al.. Advantages of a truly open-access data-sharing model. N Engl J Med 2017; 12: 1178–1181. - PubMed
-
- Clinical Study Data Request (CSDR). Clinical Study Data Request, 2020, https://clinicalstudydatarequest.com/
-
- The Yale University. Yale University Open Data Access (YODA) Project, 2020, http://yoda.yale.edu/
MeSH terms
LinkOut - more resources
Full Text Sources
Medical