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

Abstract

AbstractsBackgroundIndividual-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.MethodsEight 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.ResultsRetrospective 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.ConclusionNew 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.

A comparative analysis of the requirements for the use of data in biobanks based in Finland, Germany, the Netherlands, Norway and the United Kingdom

“…75 In particular, it has proposed a new approach, named DataSHIELD, 76 a federated infrastructure allowing researchers to jointly analyse harmonized data whilst retaining individual-level data within their respective host institutions. 77 Such a system enables to share data for research purposes without the need to physically pool them together, therefore avoiding to incur in the traditional legal constraints.…”

DataSHIELD – New Directions and Dimensions

“…Following initial proof-of-principle (Wolfson et al 2010;Jones et al 2012;Gaye et al 2014;Doiron et al 2013) a stable platform has been developed (available under a GPL3 license) and the legal, ethical and social issues arising from the DataSHIELD approach to the analysis of biomedical data have been reviewed Budin-Ljøsne et al 2014;Murtagh et al 2012Murtagh et al , 2016. DataSHIELD has been successfully piloted within two epidemiological projects in the FP7-funded BioSHaRE-EU consortium 7 -co-analysing phenotypic data from separately located European biobanks investigating i) healthy obesity comprising 10 biobanks with 99 phenotypic variables and ii) the effect of environmental determinants on health comprising five biobanks with 51 phenotypic variables and 14 environmental variables extracted from exposure models Zijlema et al 2016).…”

Protecting Personal Data in Epidemiological Research: DataSHIELD and UK Law

“…As in the HOP study, the ethical approvals and consent materials of each participating study need to be examined to ensure that the use is acceptable [18]. In this way, studies can reassure stakeholders (e.g.…”

“…When piloting DataSHIELD as part of the HOP study, the consent materials from each biobank were reviewed to ensure that the consent language was broad enough to encompass the processing of data within the biobank for research purposes and for the sharing of anonymised data [18]. This review process continues for any new biobank joining the HOP study.…”

“…In this way DataSHIELD seeks ‘… to elide privacy concerns about sharing data' while enabling a flexible interrogation of the participating datasets [2]. It is currently being piloted in several projects, including the Healthy Obese Project (HOP), under the Seventh Framework Programme for Research-funded BioSHaRE study [18]. …”