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

Gaye, Amadou and Marcon, Yannick and Isaeva, Julia and LaFlamme, Philippe and Turner, Andrew and Jones, Elinor M and Minion, Joel and Boyd, Andrew W and Newby, Christopher J and Nuotio, Marja-Liisa and Wilson, Rebecca and Butters, Oliver and Murtagh, Barnaby and Demir, Ipek and Doiron, Dany and Giepmans, Lisette and Wallace, Susan E and Budin-Ljøsne, Isabelle and Oliver Schmidt, Carsten and Boffetta, Paolo and Boniol, Mathieu and Bota, Maria and Carter, Kim W and deKlerk, Nick and Dibben, Chris and Francis, Richard W and Hiekkalinna, Tero and Hveem, Kristian and Kvaløy, Kirsti and Millar, Sean and Perry, Ivan J and Peters, Annette and Phillips, Catherine M and Popham, Frank and Raab, Gillian and Reischl, Eva and Sheehan, Nuala and Waldenberger, Melanie and Perola, Markus and van den Heuvel, Edwin and Macleod, John and Knoppers, Bartha M and Stolk, Ronald P and Fortier, Isabel and Harris, Jennifer R and Woffenbuttel, Bruce HR and Murtagh, Madeleine J and Ferretti, Vincent and Burton, Paul R (2014) DataSHIELD : taking the analysis to the data, not the data to the analysis. International Journal of Epidemiology, 43 (6). pp. 1929-1944. ISSN 0300-5771

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    Abstract

    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. 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. 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. 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.