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DataSHIELD: resolving a conflict in contemporary bioscience--performing a pooled analysis of individual-level data
Michael Wolfson1, Susan E Wallace, Nicholas Masca
1Statistics Canada, Ottawa, Ontario, Canada.
International Journal of Epidemiology
|July 16, 2010
Summary
DataSHIELD enables pooled analysis of individual-level data across studies without compromising privacy. This approach overcomes ethico-legal barriers, facilitating larger sample sizes for bioscience and health research.
Area of Science:
- Biosciences
- Health Services Research
- Socio-economic Research
Background:
- Contemporary research requires large sample sizes, often necessitating pooled analysis of data from multiple studies.
- Ethico-legal constraints and privacy legislation frequently prevent the sharing of individual-level data, creating a conflict between scientific needs and legal requirements.
Purpose of the Study:
- To present DataSHIELD, a novel approach for analyzing pooled individual-level data.
- To overcome ethico-legal barriers that hinder data sharing for pooled analyses.
Main Methods:
- Data aggregation using anonymous summary statistics from harmonized individual-level databases (DataSHIELD).
- Utilizes parallelized analysis and distributed computing.
- Leverages the updating algorithm for generalized linear models (GLMs) in specific settings.
Main Results:
- DataSHIELD provides a method to circumvent data sharing restrictions for pooled analysis.
- The conceptual use of DataSHIELD is demonstrated through two distinct research settings.
Conclusions:
- Pooled individual-level data analysis is crucial for advancing the study of complex chronic disease pathways.
- Encourages further exploration and extension of DataSHIELD's capabilities, including handling data from the same individuals across different sources.
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