DataSHIELD: taking the analysis to the data, not the data to the analysis

Abstract

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data.Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC.Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach.Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

“…An example is to prevent re-identification by 'taking the analysis to the data, not the data to the analysis', as facilitated by the initiative called dataSHIELD. 64 It is claimed that under DataSHIELD personal data re-use, linkage and analysis is enabled in accordance with legislation and guidance in the United Kingdom, primarily because no identifying or sensitive information is returned to the researcher. [65][66][67] Significant challenges however need to be overcome in the implementation of this initiative.…”

“…[65][66][67] Significant challenges however need to be overcome in the implementation of this initiative. 64 WAYS FORWARD OUTSIDE THE CONSENT OR ANONYMISE PARADIGM An alternative approach is to search for ways forward outside the consent or anonymise paradigm, by creating another legal basis than consent for the processing of sensitive personal data for medical research purposes. According to Article 81 (2a) of the Parliament's draft GDPR, such a research exemption from consent should be provided by national law, for 'research that serves a high public interest'.…”

DataSHIELD – New Directions and Dimensions

“…3 has been created to address the additional requirement in the biomedical and social sciences to co-analyse microdata that may be sensitive from different sources, without physically sharing the data (Wolfson et al 2010;Gaye et al 2014). It is an infrastructure for distributed analysis that facilitates the direct access-analysis of repository data from multiple studies simultaneously.…”

“…As such, it is possible to import data and data dictionaries into Opal (on the DataSHIELD server) using a variety of file formats including commaseparated values (.csv), Microsoft Excel (.xls), SPSS data file (.sav) as well as SQL tables. Once imported, the Gaye et al 2014). If there is no data partitioning (a) then the data can be analysed all together, if the data is partitioned horizontally (b) or vertically (c) then computational or statistical methods to co-analyse the data must be employed.…”

Privacy, security, and the public health researcher in the era of electronic health record research

“…In such systems, data live in a secured distributed system and the questions of those data (i.e., the analysis) are asked remotely [14][15][16][17]. While this may be attractive from an institutional perspective, the statistical and analytic techniques provided in existing solutions may be insufficient to perform typical epidemiological analyses.…”