POP-REFINE:在临床试验中评估和优化代表性的综合框架
Corey M Benedum1, Somnath Sarkar2,3, Selen Bozkurt4
1Genentech, Inc., South San Francisco, CA, USA.
Clinical pharmacology and therapeutics
|December 28, 2024
概括
临床试验往往缺乏多样化的群体,导致健康不平等. 一个新的框架,人口优化,代表性评估和微调 (POREF),量化和提高试验代表性,以获得公平的治疗.
科学领域:
- 临床试验方法论 临床试验方法论
- 卫生公平研究 卫生公平研究
- 生物统计学 生物统计学
背景情况:
- 从历史上看,临床试验代表不同人群不足,延续了健康不平等.
- 确保代表性对于治疗的适当性,对新疗法的公平获取以及研究结果的概括性至关重要.
- 监管机构越来越多地关注新疗法在所有患者群体中的影响.
研究的目的:
- 引入一个新的框架来量化和提高临床试验人群的代表性.
- 为了解决测量和优化临床试验多样性的欠发达的系统方法.
- 支持监管和内部决策流程,以实现更具包容性的临床研究.
主要方法:
- 开发了人口优化,代表性评估和微调 (POREF) 框架.
- 包括评估整体和子组代表性的方法.
- 将框架应用于九项瘤学试验,使用非识别的电子健康记录数据库来量化符合条件的人口代表性.
主要成果:
- 量化了九项瘤学临床试验的合格人群的代表性.
- 证明了框架能够识别非代表性驱动因素的能力.
- 展示了优化资格标准,以实现更具代表性的患者群体.
结论:
- 该POREF框架提供了一个全面的,数据驱动的方法,以提高临床试验的代表性.
- 这种系统方法可以提高研究结果的概括性,并减少营销后的差异.
- 该框架可以适应各种疾病征兆,并且可以扩展到评估注册样本.
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