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. 2013 Nov 21;10(1):12.
doi: 10.1186/1742-7622-10-12.

Data harmonization and federated analysis of population-based studies: the BioSHaRE project

Dany Doiron  1   2 , Paul Burton  3 , Yannick Marcon  1 , Amadou Gaye  3 , Bruce H R Wolffenbuttel  4 , Markus Perola  5   6 , Ronald P Stolk  7 , Luisa Foco  8 , Cosetta Minelli  9 , Melanie Waldenberger  10 , Rolf Holle  10 , Kirsti Kvaløy  11 , Hans L Hillege  12 , Anne-Marie Tassé  2 , Vincent Ferretti #  13 , Isabel Fortier #  1
Affiliations

Data harmonization and federated analysis of population-based studies: the BioSHaRE project

Dany Doiron et al. Emerg Themes Epidemiol. .

Abstract

Background: Individual-level data pooling of large population-based studies across research centres in international research projects faces many hurdles. The BioSHaRE (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) project aims to address these issues by building a collaborative group of investigators and developing tools for data harmonization, database integration and federated data analyses.

Methods: Eight population-based studies in six European countries were recruited to participate in the BioSHaRE project. Through workshops, teleconferences and electronic communications, participating investigators identified a set of 96 variables targeted for harmonization to answer research questions of interest. Using each study's questionnaires, standard operating procedures, and data dictionaries, harmonization potential was assessed. Whenever harmonization was deemed possible, processing algorithms were developed and implemented in an open-source software infrastructure to transform study-specific data into the target (i.e. harmonized) format. Harmonized datasets located on server in each research centres across Europe were interconnected through a federated database system to perform statistical analysis.

Results: Retrospective harmonization led to the generation of common format variables for 73% of matches considered (96 targeted variables across 8 studies). Authenticated investigators can now perform complex statistical analyses of harmonized datasets stored on distributed servers without actually sharing individual-level data using the DataSHIELD method.

Conclusion: New Internet-based networking technologies and database management systems are providing the means to support collaborative, multi-center research in an efficient and secure manner. The results from this pilot project show that, given a strong collaborative relationship between participating studies, it is possible to seamlessly co-analyse internationally harmonized research databases while allowing each study to retain full control over individual-level data. We encourage additional collaborative research networks in epidemiology, public health, and the social sciences to make use of the open source tools presented herein.

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Figures

Figure 1
Figure 1
Example of data processing to obtain a common format: deriving the harmonized Fasting Glucose DataSchema variable for two studies.
Figure 2
Figure 2
Data harmonization and federated infrastructure for three HOP studies.
See this image and copyright information in PMC

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