使用临床数据交换标准联盟 (CDISC) 研究数据表格模型整合现有队列研究数据的实用方法:案例研究
Keiichi Matsuzaki1, Megumi Kitayama2, Keiichi Yamamoto3
1Department of Public Health, School of Medicine, Kitasato University, Sagamihara, Japan.
JMIR medical informatics
|December 28, 2023
概括
整合遗留数据是一项挑战. 这项研究表明,临床数据交换标准联盟 (CDISC) 研究数据表格模型 (SDTM) 有效地集成多个现有数据库,简化了聚合分析.
科学领域:
- 数据科学数据科学数据科学
- 生物医学信息学 生物医学信息学
- 临床研究信息学
背景情况:
- 遗留数据的整合带来了方法上的挑战.
- 标准化数据格式对于重新分析各种数据集至关重要.
- 之前的工作开发了用于从假设试验中生成SDTM数据的工具.
研究的目的:
- 设计一个实用的模型来整合已经存在的数据库.
- 利用临床数据交换标准联盟 (CDISC) 的研究数据表格模型 (SDTM) 进行数据协调.
- 建立一个可重复的方法来组合用于不同目的收集的数据.
主要方法:
- 数据集成涉及变量确认,SDTM映射和SDTM数据生成.
- 包括域名,变量名称和测试代码在内的元数据被嵌入到研究电子数据捕获 (REDCap) 注释中.
- 运营数据模型 (ODM) 格式被用于数据字典,而REDCap2SDTM版本2被用于最终数据生成.
主要成果:
- 在3个独立的现有数据库中,SDTM成功生成了7个域的数据.
- 总共有17个共同项目被绘制出来,证明了成功的协调.
- 三个不同的数据库被整合到一个单一的,标准化的CDISC SDTM格式数据库中.
结论:
- CDISC SDTM提供了一个强大的框架,用于整合多个现有数据库.
- 这种方法有助于有效地聚合和重新分析旧数据.
- 开发的模型为协调各种临床试验数据集提供了一个实际的解决方案.
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