Keeping Watch

Tarran, Brian

doi:10.1111/j.1740-9713.2014.00763.x

Cited by 3 publications

(3 citation statements)

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“…Aggarwal and Yu [22] and the Institute of Medicine [7] provide examples of approaches to achieve anonymity of personal health information, including the following:

Transformation of data sets using data reduction techniques such as generalisation of the data by grouping of values into categories, and suppression/masking of data where specific values or whole records are removed from the dataset. Data perturbation techniques can also be applied, whereby random noise is added to the true values.

Diversity/closeness models have been developed with the aim of transforming data to ensure that specific individuals cannot be identified within public databases [23]. These provide a guarantee of a pre-specified level of anonymity based on non-uniqueness of records within the transformed data.…”

Section: Introductionmentioning

confidence: 99%

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

Tucker

Branson

Dilleen

et al. 2016

BMC Med Res Methodol

107

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BackgroundGreater 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.DiscussionOur key recommendations are as follows: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.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.SummaryThis 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.

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“…Aggarwal and Yu [22] and the Institute of Medicine [7] provide examples of approaches to achieve anonymity of personal health information, including the following:

Section: Introductionmentioning

confidence: 99%

“…Diversity/closeness models have been developed with the aim of transforming data to ensure that specific individuals cannot be identified within public databases [23]. These provide a guarantee of a pre-specified level of anonymity based on non-uniqueness of records within the transformed data.…”

Section: Introductionmentioning

confidence: 99%

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

Tucker

Branson

Dilleen

et al. 2016

BMC Med Res Methodol

107

View full text Add to dashboard Cite

show abstract

“…This reflects discussions in the context of ‘big data’. However, it is clear that current anonymisation methods are imperfect and that re-identification is possible [23]. …”

Section: Introductionmentioning

confidence: 99%

Sharing data from clinical trials: the rationale for a controlled access approach

Sydes

Johnson

Meredith

et al. 2015

Trials

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BackgroundThe move towards increased transparency around clinical trials is welcome. Much focus has been on under-reporting of trials and access to individual patient data to allow independent verification of findings. There are many other good reasons for data sharing from clinical trials. We describe some key issues in data sharing, including the challenges of open access to data. These include issues in consent and disclosure; risks in identification, including self-identification; risks in distorting data to prevent self-identification; and risks in analysis. These risks have led us to develop a controlled access policy, which safeguards the rights of patients entered in our trials, guards the intellectual property rights of the original researchers who designed the trial and collected the data, provides a barrier against unnecessary duplication, and ensures that researchers have the necessary resources and skills to analyse the data.MethodsWe briefly discuss the practicalities of our current approach to data sharing, including ensuring that data are discoverable and how to deal with old studies. We describe data sharing activities at the MRC Clinical Trials Unit.ResultsOne hundred and three data sharing activities were logged from 2012 to 2014 from external and internal applicants. The motivations are varied, but none have been for replication of the primary results.ConclusionsFor any request to share data, we note the important role of independent reviewers as well as reviewers who know the study well, and present some of the key questions that all reviewers should ask when deciding whether a request is reasonable. We consider the responsibilities of all parties. We highlight the potential for opportunity costs. Clinical trial data should be shared for reasonable requests but there are many practical issues that must be explicitly considered.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-015-0604-6) contains supplementary material, which is available to authorized users.

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El seguimiento activo de las series de ficción en internet. La atención y la emoción como desencadenantes del binge-watching

Arrojo

Martín²

2019

RCom

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Se investiga cómo influyen los nuevos diseños comunicativos en el entorno digital, en la percepción de los contenidos comunicativos y en el seguimiento activo (engagement) de los mismos. El estudio plantea dos objetivos: 1) analizar cómo el soporte (teléfono, ordenador, televisor) condiciona la percepción y el engagement de las series de ficción; y 2) cómo estos soportes tecnológicos pueden condicionar el consumo. Para realizar la investigación, se eligió la serie “The 100”, y se analizaron las diferencias en la percepción en función del canal de distribución —sujeto a una parrilla de programación o no— y la repercusión del nivel de consumo según el soporte tecnológico utilizado. Como método científico, se empleó la Metodología de Investigación del Neuromarketing aplicada al estudio de fenómenos comunicativos. Los resultados corroboran la hipótesis de que el soporte y el dispositivo de transmisión condicionan la atención y el seguimiento activo (engagement) del contenido audiovisual en Internet.

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Keeping Watch

Abstract: Brian Tarran investigates the role of statistical models in the ongoing fight against hackers, crackers and assorted cyber attackers – those who are intent on stealing our personal data from computer networks.

Cited by 3 publications

References 0 publications

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

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

Sharing data from clinical trials: the rationale for a controlled access approach

El seguimiento activo de las series de ficción en internet. La atención y la emoción como desencadenantes del binge-watching

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