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Protecting patient privacy when sharing patient-level data from clinical trials

Overview of attention for article published in BMC Medical Research Methodology, July 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

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4 X users
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1 patent
wikipedia
2 Wikipedia pages

Citations

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82 Dimensions

Readers on

mendeley
133 Mendeley
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1 CiteULike
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Title
Protecting patient privacy when sharing patient-level data from clinical trials
Published in
BMC Medical Research Methodology, July 2016
DOI 10.1186/s12874-016-0169-4
Pubmed ID
Authors

Katherine Tucker, Janice Branson, Maria Dilleen, Sally Hollis, Paul Loughlin, Mark J. Nixon, Zoë Williams

Abstract

Greater transparency and, in particular, sharing of patient-level data for further scientific research is an increasingly important topic for the pharmaceutical industry and other organisations who sponsor and conduct clinical trials as well as generally in the interests of patients participating in studies. A concern remains, however, over how to appropriately prepare and share clinical trial data with third party researchers, whilst maintaining patient confidentiality. Clinical trial datasets contain very detailed information on each participant. Risk to patient privacy can be mitigated by data reduction techniques. However, retention of data utility is important in order to allow meaningful scientific research. In addition, for clinical trial data, an excessive application of such techniques may pose a public health risk if misleading results are produced. After considering existing guidance, this article makes recommendations with the aim of promoting an approach that balances data utility and privacy risk and is applicable across clinical trial data holders. Our key recommendations are as follows: 1. Data anonymisation/de-identification: Data holders are responsible for generating de-identified datasets which are intended to offer increased protection for patient privacy through masking or generalisation of direct and some indirect identifiers. 2. Controlled access to data, including use of a data sharing agreement: A legally binding data sharing agreement should be in place, including agreements not to download or further share data and not to attempt to seek to identify patients. Appropriate levels of security should be used for transferring data or providing access; one solution is use of a secure 'locked box' system which provides additional safeguards. This article provides recommendations on best practices to de-identify/anonymise clinical trial data for sharing with third-party researchers, as well as controlled access to data and data sharing agreements. The recommendations are applicable to all clinical trial data holders. Further work will be needed to identify and evaluate competing possibilities as regulations, attitudes to risk and technologies evolve.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 <1%
Unknown 132 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 11%
Student > Master 15 11%
Other 13 10%
Student > Bachelor 13 10%
Researcher 11 8%
Other 17 13%
Unknown 49 37%
Readers by discipline Count As %
Medicine and Dentistry 15 11%
Computer Science 10 8%
Agricultural and Biological Sciences 7 5%
Biochemistry, Genetics and Molecular Biology 6 5%
Pharmacology, Toxicology and Pharmaceutical Science 5 4%
Other 36 27%
Unknown 54 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 December 2018.
All research outputs
#3,420,662
of 23,577,654 outputs
Outputs from BMC Medical Research Methodology
#539
of 2,081 outputs
Outputs of similar age
#62,819
of 357,125 outputs
Outputs of similar age from BMC Medical Research Methodology
#14
of 38 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,081 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 357,125 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.