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

Amadou Gaye1, Yannick Marcon1, Julia Isaeva1

  • 1School of Social and Community Medicine, University of Bristol, Bristol, UK, Maelstrom Research Group, Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada, Norwegian Institute of Public Health, Oslo, Norway, Department Statistical Science, University College London, London, UK, Department of Infection, Immunity and Inflammation, Health Sciences, University of Leicester, Leicester, UK, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland, Department of Health Sciences, University of Leicester, Leicester, UK, Department of Sociology, University of Leicester, Leicester, UK, Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands, Institut für Community Medicine, University Medicine of Greifswald, Greifswald, Germany, International Prevention Research Institute, Lyon, France, Telethon Kids Institute, University of Western Australia, Perth, WA, Australia, School of Geosciences, University of Edinburgh, Edinburgh, UK, Norwegian University of Science and Technology, Levanger, Norway, HRB Centre for Diet and Health Research, Department of Epidemiology and Public Health, University College Cork, Cork, Ireland, Research Unit of Molecular Epidemiology, Research Center for Environmental Health, Neuherberg, Germany, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK, University of Tartu, Estonian Genome Center, Tartu, Estonia, University Medical Center Groningen, Medical Statistics, Groningen, The Netherlands, Centre of Genomics and Policy, McGill University, Montreal, Canada, University Medical Center Groningen, LifeLines Cohort Study, Groningen, The Netherlands, Department of Endocrinology, University Medical Center Groningen, Groningen, The Netherlands, School of Social and Community Medicine, University of Bristol, Bristol, UK and Onta

International Journal of Epidemiology
|September 29, 2014
PubMed
Summary
This summary is machine-generated.

DataSHIELD enables secure, pooled data analysis across multiple studies without centralizing sensitive information. This technology addresses privacy concerns and facilitates large-scale biomedical and social science research.

Keywords:
DataSHIELDELSIbioinformaticsconfidentialitydisclosuredistributed computingintellectual propertypooled analysisprivacy

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Area of Science:

  • Biomedical and Social Science Research
  • Data Governance and Privacy

Background:

  • Large-scale research necessitates pooled data analysis, raising ethical and legal concerns regarding privacy, confidentiality, and intellectual property.
  • Initiatives like the UK's 'care.data' highlight societal and professional debates surrounding data sharing.
  • DataSHIELD offers a technological solution to challenges in accessing individual-level data for research.

Purpose of the Study:

  • To present DataSHIELD as a novel technological solution for pooled data analysis.
  • To describe the technical implementation of DataSHIELD.
  • To illustrate the application and implications of DataSHIELD in real-world research settings.

Main Methods:

  • Utilizes a central analysis computer (AC) and multiple data computers (DCs) storing distributed datasets.
  • Employs parallelized, simultaneous analysis of data sets across DCs.
  • Leverages non-disclosive summary statistics and commands for inter-computer communication.
  • Technical implementation involves a modified R statistical environment linked to an Opal database within each DC's firewall.

Main Results:

  • DataSHIELD is currently operational for federated analysis of 10 datasets across eight European countries.
  • The Healthy Obese Project and Environmental Core Project (BioSHaRE-EU) utilize this approach.
  • Demonstrates the practical opportunities and inherent challenges of the DataSHIELD methodology.

Conclusions:

  • Facilitates necessary co-analysis of individual-level data when governance restrictions impede data sharing or access.
  • Protects intellectual property by enabling data sharing for analysis without transferring physical data, crucial for vulnerable research groups.
  • Overcomes logistical barriers posed by large dataset sizes that preclude direct transfer for analysis.