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首页> 外文期刊>American Journal of Epidemiology >Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach
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Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach

机译:在癌症流行病学研究中寻求严格的数据协调:一种方法

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摘要

Cancer epidemiologists have a long history of combining data sets in pooled analyses, often harmonizing heterogeneous data from multiple studies into 1 large data set. Although there are useful websites on data harmonization with recommendations and support, there is little research on best practices in data harmonization; each project conducts harmonization according to its own internal standards. The field would be greatly served by charting the process of data harmonization to enhance the quality of the harmonized data. Here, we describe the data harmonization process utilized at the Fred Hutchinson Cancer Research Center (Seattle, Washington) by the coordinating centers of several research projects. We describe a 6-step harmonization process, including: 1) identification of questions the harmonized data set is required to answer; 2) identification of high-level data concepts to answer those questions; 3) assessment of data availability for data concepts; 4) development of common data elements for each data concept; 5) mapping and transformation of individual data points to common data elements; and 6) quality-control procedures. Our aim here is not to claim a "correct" way of doing data harmonization but to encourage others to describe their processes in order that we can begin to create rigorous approaches. We also propose a research agenda around this issue.
机译:癌症流行病学家在合并分析中组合数据集的历史悠久,通常将来自多个研究的异类数据统一为一个大数据集。尽管有有用的网站提供建议和支持,但很少有关于数据统一最佳实践的研究。每个项目都根据自己的内部标准进行协调。通过绘制数据协调过程的图表来提高协调数据的质量,将极大地为该领域服务。在这里,我们描述了几个研究项目的协调中心在弗雷德·哈钦森癌症研究中心(华盛顿州西雅图)使用的数据协调过程。我们描述了一个由6个步骤组成的统一过程,包括:1)确定统一数据集需要回答的问题; 2)确定高级数据概念以回答这些问题; 3)评估数据概念的数据可用性; 4)为每个数据概念开发通用数据元素; 5)将单个数据点映射和转换为通用数据元素; 6)质量控制程序。我们的目的不是要声明一种“正确”的数据协调方式,而是要鼓励其他人描述他们的过程,以便我们可以开始创建严格的方法。我们还针对此问题提出了研究议程。

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