一个因果路线图,用于生成高质量的现实世界的证据
Lauren E Dang1, Susan Gruber2, Hana Lee3
1Department of Biostatistics, University of California, Berkeley, CA, USA.
Journal of clinical and translational science
|October 30, 2023
概括
因果路线图为设计使用真实世界数据 (RWD) 来生成高质量的真实世界证据 (RWE) 的研究提供了一个结构化的过程. 这一框架提高了透明度,并有助于与监管机构的沟通.
科学领域:
- 临床研究方法论临床研究方法论
- 健康数据科学健康数据科学
- 监管科学是一种监管科学.
背景情况:
- 在临床政策和监管决策中,越来越多地使用现实世界的证据 (RWE).
- 指导的扩散,但现实世界数据 (RWD) 的不一致性,研究建议和分析.
- 方法上的缺陷和不可思议的假设往往会影响RWD分析.
研究的目的:
- 引入和扩大临床和转化研究人员的因果路线图框架.
- 提供一个结构化,代的过程,预先指定研究设计和分析计划用于RWE生成.
- 促进因果假设的透明评估和设计选择的比较.
主要方法:
- 因果路线图提供了一个明确的,详细的和代的过程.
- 它指导研究人员预先确定研究设计和分析计划.
- 该框架支持对因果假设的透明评估和对选择的客观比较.
主要成果:
- 因果路线图有助于评估研究证据的可能质量.
- 它有助于确定旨在产生高质量的RWE的研究.
- 促进与监管机构和利益相关方的有效沟通.
结论:
- 因果路线图是提高RWE研究的严谨性和透明度的宝贵工具.
- 它在一个单一的,全面的框架内解决了广泛的指导问题.
- 该框架支持生成可靠的RWE用于关键决策.
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