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临床试验中的数据共享——试验数据集匿名化实用指南。

Data sharing in clinical trials - practical guidance on anonymising trial datasets.

作者信息

Keerie Catriona, Tuck Christopher, Milne Garry, Eldridge Sandra, Wright Neil, Lewis Steff C

机构信息

Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Nine Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK.

Queen Mary University of London, London, UK.

出版信息

Trials. 2018 Jan 10;19(1):25. doi: 10.1186/s13063-017-2382-9.

Abstract

BACKGROUND

There is an increasing demand by non-commercial funders that trialists should provide access to trial data once the primary analysis is completed. This has to take into account concerns about identifying individual trial participants, and the legal and regulatory requirements.

METHODS

Using the good practice guideline laid out by the work funded by the Medical Research Council Hubs for Trials Methodology Research (MRC HTMR), we anonymised a dataset from a recently completed trial. Using this example, we present practical guidance on how to anonymise a dataset, and describe rules that could be used on other trial datasets. We describe how these might differ if the trial was to be made freely available to all, or if the data could only be accessed with specific permission and data usage agreements in place.

RESULTS

Following the good practice guidelines, we successfully created a controlled access model for trial data sharing. The data were assessed on a case-by-case basis classifying variables as direct, indirect and superfluous identifiers with differing methods of anonymisation assigned depending on the type of identifier. A final dataset was created and checks of the anonymised dataset were applied. Lastly, a procedure for release of the data was implemented to complete the process.

CONCLUSIONS

We have implemented a practical solution to the data anonymisation process resulting in a bespoke anonymised dataset for a recently completed trial. We have gained useful learnings in terms of efficiency of the process going forward, the need to balance anonymity with data utilisation and future work that should be undertaken.

摘要

背景

非商业资助者对试验者的需求日益增加,要求他们在完成主要分析后提供试验数据的访问权限。这必须考虑到对识别个体试验参与者的担忧以及法律和监管要求。

方法

利用医学研究理事会试验方法研究中心(MRC HTMR)资助的工作所制定的良好实践指南,我们对最近完成的一项试验的数据集进行了匿名化处理。以这个例子为基础,我们提供了关于如何对数据集进行匿名化处理的实用指南,并描述了可用于其他试验数据集的规则。我们描述了如果试验要向所有人免费提供,或者数据只能在有特定许可和数据使用协议的情况下访问,这些规则可能会有何不同。

结果

遵循良好实践指南,我们成功创建了一个试验数据共享的受控访问模型。根据具体情况对数据进行评估,将变量分类为直接标识符、间接标识符和多余标识符,并根据标识符的类型采用不同的匿名化方法。创建了最终数据集,并对匿名化数据集进行了检查。最后,实施了数据发布程序以完成整个过程。

结论

我们为数据匿名化过程实施了一个切实可行的解决方案,为最近完成的一项试验生成了一个定制的匿名数据集。我们在该过程的效率、平衡匿名性与数据利用的必要性以及未来应开展的工作方面获得了有益的经验教训。

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