DataSHIELD: taking the analysis to the data not the data to the analysis
46 mins 59 secs,
85.94 MB,
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| Description: |
Burton, P (University of Bristol, University of Bristol)
Thursday 8th December 2016 - 14:15 to 15:00 |
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| Created: | 2016-12-19 12:30 |
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| Collection: | Data Linkage and Anonymisation |
| Publisher: | Isaac Newton Institute |
| Copyright: | Burton, P |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Research in modern biomedicine and social science often requires sample sizes so large that they can 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 governance questions and can be controversial. These reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that circumvents 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) that store the data to be co-analysed. Each DC is located at one of the studies contributing data to the analysis. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands that are transmitted back and forth between the DCs and the AC. Technical implementation of DataSHIELD employs a specially modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is then controlled through a standard R environment at the AC. DataSHIELD is most often configured to carry out a – typically fully-efficient – analysis that is mathematically equivalent to placing all data from all studies in one central database and analysing them all together (with centre-effects, of course, where required). Alternatively, it can be set up for study-level meta-analysis: estimates and standard errors are derived independently from each study and are subject to centralized random effects meta-analysis at the AC. DataSHIELD is being developed as a flexible, easily extendible, open-source way to provide secure data access to a single study or data repository as well as for settings involving several studies. Although the talk will focus on the version of DataSHIELD that represents our current standard implementation, it will also explore some of our recent thinking in relation to issues such as vertically partitioned (record linkage) data, textual data and non-disclosive graphical visualisation. |
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