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.

IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Clinical Trials Pub Date : 2025-02-01 Epub Date: 2024-06-19 DOI:10.1177/17407745241259086
Aryelly Rodriguez, Steff C Lewis, Sandra Eldridge, Tracy Jackson, Christopher J Weir
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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.

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调查英国研究人员对临床试验数据集的去身份化、匿名化、发布方法和再识别风险估计的看法。
背景:来自临床试验的匿名数据集在科学界共享的压力越来越大。然而,对于如何匿名化和准备临床试验数据集以供共享,目前还没有一套标准化的建议,而越来越多的匿名数据集正可用于二次研究。我们的目的是探讨目前英国研究人员对临床试验数据集的去识别、匿名化、发布方法和再识别风险评估的看法和经验。方法:我们采用在线探索性横断面描述性调查,包括开放式和封闭式问题。结果:从2022年6月到2022年10月,我们收到了38份邀请函。然而,35名参与者(92%)使用内部文档和发表的指南来去识别/匿名临床试验数据集。18名(47%)参与者报告说,去识别化,然后是匿名化,然后在授予访问权限之前满足数据持有者的要求(受控访问),这是发布数据集的最常见过程。然而,11名参与者(29%)先前有重新识别风险评估的知识,但他们没有使用任何方法。在数据集去识别/匿名化和维护这些数据集的过程中,经验大多是负面的,报告的主要问题是缺乏资源、指导和培训。结论:大多数应答者报告使用文件化流程去识别和匿名化。然而,我们的调查结果清楚地表明,在满足去识别/匿名数据集的共享要求方面,在指导、资源和培训方面仍然存在差距,并且再识别风险评估是一个不发达的领域。
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来源期刊
Clinical Trials
Clinical Trials 医学-医学:研究与实验
CiteScore
4.10
自引率
3.70%
发文量
82
审稿时长
6-12 weeks
期刊介绍: Clinical Trials is dedicated to advancing knowledge on the design and conduct of clinical trials related research methodologies. Covering the design, conduct, analysis, synthesis and evaluation of key methodologies, the journal remains on the cusp of the latest topics, including ethics, regulation and policy impact.
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