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

DataSHIELD: taking the analysis to the data, not the data to the analysis.
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DOI:
10.1093/ije/dyu188
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发表时间:
2014-12
影响因子:
7.7
通讯作者:
Burton PR
Burton PR
中科院分区:
医学1区
文献类型:
--
作者:
Gaye A;Marcon Y;Isaeva J;LaFlamme P;Turner A;Jones EM;Minion J;Boyd AW;Newby CJ;Nuotio ML;Wilson R;Butters O;Murtagh B;Demir I;Doiron D;Giepmans L;Wallace SE;Budin-Ljøsne I;Oliver Schmidt C;Boffetta P;Boniol M;Bota M;Carter KW;deKlerk N;Dibben C;Francis RW;Hiekkalinna T;Hveem K;Kvaløy K;Millar S;Perry IJ;Peters A;Phillips CM;Popham F;Raab G;Reischl E;Sheehan N;Waldenberger M;Perola M;van den Heuvel E;Macleod J;Knoppers BM;Stolk RP;Fortier I;Harris JR;Woffenbuttel BH;Murtagh MJ;Ferretti V;Burton PR

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背景资料:现代生物医学和社会科学的研究需要如此大的样本量,以至于它们通常只能通过对几项研究的数据进行汇总共同分析来实现。但是,将个人信息集中在一个中央数据库中,研究人员可能会查询这些信息,这引发了重要的伦理法律问题,可能会引起争议。在英国,最近与英国拟议的“care.data”倡议相关的辩论和争议凸显了这一点,这些问题反映了社会和专业人士对隐私、保密性和知识产权的重要担忧。DataSHIELD提供了一种新的技术解决方案,可以规避一些最基本的挑战,促进研究人员和其他医疗保健专业人员访问个人水平的数据。方法:命令从中央分析计算机(AC)发送到存储待共同分析的数据的多个数据计算机(DC)。这些数据集是同时并行分析的。独立的并行分析通过DC和AC之间来回传输的非重复性汇总统计和命令链接。本文介绍了DataSHIELD的技术实现,使用修改后的R统计环境链接到一个蛋白石数据库部署在每个DC的计算机防火墙后面。分析通过AC的标准R环境进行控制。结果如下:基于Opal/R的实现,DataSHIELD目前被健康肥胖项目和环境核心项目(BioSHaRE-EU)用于对8个欧洲国家的10个数据集进行联合分析,这说明了DataSHIELD方法带来的机遇和挑战。结论:DataSHIELD有助于在以下情况下开展重要研究:㈠从科学上有必要对来自多项研究的个人层面数据进行共同分析,但治理限制禁止发布或分享某些所需数据,和/或使数据访问速度慢得令人无法接受;(二)研究小组(例如在发展中国家)特别容易受到知识产权损失的影响-研究人员希望与国家和国际合作者充分分享其数据中的信息,但不希望自己交出物理数据;以及(iii)数据集将被包括在个体水平的共同分析中,但是数据的物理大小排除了直接转移到新的地点进行分析。
Background: Research in modern biomedicine and social science requires sample sizes so large that they can often 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 ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.
DOI: 10.1086/605454
发表时间: 2009-08-01
影响因子: 11.8
作者:
Burman, William;Daum, Robert;Natarajan, Padma
通讯作者: Natarajan, Padma
DOI: 10.1002/sta4.19
发表时间: 2013-01-01
期刊: STAT
影响因子: 1.7
作者:
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DOI: 10.1186/1742-7622-10-12
发表时间: 2013-11-21
影响因子: 2.3
作者:
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通讯作者: Fortier I
DOI: 10.1159/000336673
发表时间: 2012-01-01
影响因子: 1.7
作者:
Murtagh, M. J.;Demir, I.;Burton, P. R.
通讯作者: Burton, P. R.
DOI: 10.1038/ng.361
发表时间: 2009-06
期刊: NATURE GENETICS
影响因子: 30.8
作者:
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