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首页> 美国卫生研究院文献>International Journal of Epidemiology >DataSHIELD: resolving a conflict in contemporary bioscience—performing a pooled analysis of individual-level data without sharing the data
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DataSHIELD: resolving a conflict in contemporary bioscience—performing a pooled analysis of individual-level data without sharing the data

机译：DataSHIELD：解决当代生物科学中的冲突-在不共享数据的情况下对个人数据进行汇总分析

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>Background Contemporary bioscience sometimes demands vast sample sizes and there is often then no choice but to synthesize data across several studies and to undertake an appropriate pooled analysis. This same need is also faced in health-services and socio-economic research. When a pooled analysis is required, analytic efficiency and flexibility are often best served by combining the individual-level data from all sources and analysing them as a single large data set. But ethico-legal constraints, including the wording of consent forms and privacy legislation, often prohibit or discourage the sharing of individual-level data, particularly across national or other jurisdictional boundaries. This leads to a fundamental conflict in competing public goods: individual-level analysis is desirable from a scientific perspective, but is prevented by ethico-legal considerations that are entirely valid.>Methods Data aggregation through anonymous summary-statistics from harmonized individual-level databases (DataSHIELD), provides a simple approach to analysing pooled data that circumvents this conflict. This is achieved via parallelized analysis and modern distributed computing and, in one key setting, takes advantage of the properties of the updating algorithm for generalized linear models (GLMs).>Results The conceptual use of DataSHIELD is illustrated in two different settings.>Conclusions As the study of the aetiological architecture of chronic diseases advances to encompass more complex causal pathways—e.g. to include the joint effects of genes, lifestyle and environment—sample size requirements will increase further and the analysis of pooled individual-level data will become ever more important. An aim of this conceptual article is to encourage others to address the challenges and opportunities that DataSHIELD presents, and to explore potential extensions, for example to its use when different data sources hold different data on the same individuals.
机译：>背景当代生物科学有时需要庞大的样本量，因此往往只有别的选择，只能综合多个研究的数据并进行适当的汇总分析。卫生服务和社会经济研究也面临同样的需求。当需要汇总分析时，通常可以通过组合来自所有来源的单个级别的数据并将它们作为单个大数据集进行分析来最好地提高分析效率和灵活性。但是，包括同意书的措辞和隐私权立法在内的道德与法律约束通常会禁止或阻止个人数据的共享，尤其是在国家或其他管辖范围内。这导致了竞争性公共物品的根本冲突：从科学的角度来看，个人级别的分析是可取的，但出于完全合法的伦理法律考虑而被阻止。>方法通过匿名汇总统计信息进行数据汇总统一的个人级别数据库（DataSHIELD）提供的一种简单方法可以分析池数据，从而避免这种冲突。这是通过并行分析和现代分布式计算来实现的，并且在一个关键的设置下，它利用了广义线性模型（GLM）的更新算法的属性。>结果 DataSHIELD的概念性用法在两种不同的设置。>结论随着对慢性病病因学结构的研究不断发展，涵盖了更复杂的因果路径，例如包括基因，生活方式和环境的共同影响-样本量要求将进一步增加，对汇总的个人水平数据的分析将变得越来越重要。本概念文章的目的是鼓励其他人应对DataSHIELD提出的挑战和机遇，并探索潜在的扩展，例如，当不同数据源在同一个人上拥有不同数据时，对其进行扩展。

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