药物流行病学研究在分布式网络环境中的透明度,可重复性和可复制性
Ashish Rai1, Judith C Maro1, Sarah Dutcher2
1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Pharmacoepidemiology and drug safety
|May 24, 2024
概括
美国食品和药物管理局 (FDA) 哨兵系统采用最佳实践,以现实数据进行药物安全研究. 这确保了在规模上生成监管级证据的透明度和可重复性.
科学领域:
- 药物监督 药物监督 药物监督
- 现实世界的数据分析.
- 监管科学是一种监管科学.
背景情况:
- 现实世界数据 (RWD) 为药物安全监测提供了宝贵的见解.
- 确保RWD研究的信任和可靠性对于监管决策至关重要.
- 美国食品和药物管理局 (FDA) 开发了"哨兵系统",以应对这些挑战.
研究的目的:
- 描述FDA的哨兵系统实施的使用RWD进行药物安全研究的最佳实践.
- 概述系统的方法,以确保透明度,可重复性和可复制性在现实世界的证据生成.
主要方法:
- Sentinel采用一个逐步的图表来协调数据,质量检查,查询设计,实施和报告.
- 数据合作伙伴将源数据转换为Sentinel通用数据模型 (SCDM).
- 在查询执行之前,对转换的数据进行严格的质量检查.
主要成果:
- 哨兵通用数据模型框架,数据转换代码和质量保证包是公开的.
- 哨兵的查询系统允许在没有个人级别数据交换的情况下进行复杂的分析.
- 公共可用的文档,培训和在线资源促进了系统的使用.
结论:
- 哨兵系统展示了RWD如何在规模上产生监管级证据.
- 它的透明,可重复和可复制的过程提高了对药物安全性研究的信任.
- 数据模型,代码和研究规范的公开可用性支持科学严谨性.
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