为全球观测研究网络提供可定制的数据质量工具.
Judith Lewis1, Brenna Hogan2, Charles Goss3
1Vanderbilt University Medical Center, Nashville, TN, USA.
Studies in health technology and informatics
|May 17, 2025
概括
这项研究概括了用于多站点临床数据分析的数据质量工具,使研究联盟更容易使用. 协调数据工具包可以提高数据的一致性和报告,而不需要广泛的技术专业知识.
科学领域:
- 临床数据管理 临床数据管理
- 医疗信息学 医疗信息学
- 观察性研究 观察性研究
背景情况:
- 多站点观察性临床数据分析需要对数据质量进行严格的评估.
- 现有的数据质量工具往往需要大量的技术专业知识,这限制了研究联盟的采用.
- 一般化数据质量解决方案对于提高协作研究可靠性至关重要.
研究的目的:
- 推广已建立的数据质量检查和报告生成工具,以便在各种研究网络中更容易实施.
- 减少用户网站对数据质量评估所需的技术专业知识.
- 提供一个可扩展的解决方案,用于数据质量和报告在多站点研究联盟.
主要方法:
- 利用REDCap (研究电子数据捕获) 软件来存储数据模型细节,变量期望和用户组信息.
- 使用 REDCap API 开发了一个应用程序来检索存储的细节并评估数据集的合规性,逻辑一致性和完整性.
- 使用自由可用的REDCap和R/Shiny平台构建了Harmonist数据工具包,代码可以在GitHub上访问.
主要成果:
- 一般化的Harmonist数据工具包成功地减少了对本地编程专业知识的需求.
- 所有五个协作联盟都报告了在检测不一致性和生成信息化数据报告和可视化方面取得的好处.
- 该工具包有效地评估了数据集的数据模型,逻辑一致性和完整性.
结论:
- 哈莫尼斯特数据工具包为研究联盟的数据质量和报告生成提供了有价值的,可访问的解决方案.
- 该工具包解决了在多地点观测研究中对标准化数据质量评估的需求.
- 像REDCap和R/Shiny这样的自由可用的平台可以开发出强大的,可通用的研究工具.
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