通过有针对性的学习评估和改进现实世界的证据
Susan Gruber1, Rachael V Phillips2, Hana Lee3
1Putnam Data Sciences, LLC, Cambridge, MA, USA. sgruber@putnamds.com.
目标学习路线图系统地指导真实世界的证据 (RWE) 产生和评估. 应用它揭示了因果推断的数据限制,但在明确的假设下,可以估计治疗效应.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 现实世界的证据 (RWE) 对于监管和临床决策至关重要.
- RWE来自各种来源,包括观察性研究和临床试验中的真实世界数据.
- 对RWE的有效性和可解释性的系统评估是必不可少的.
研究的目的:
- 用目标学习 (TL) 路线图来说明评估RWE有效性和可解释性的原则性方法.
- 从观察性研究中识别因果效应估计中的障碍.
- 探索克服障碍和加强RWE的方法.
主要方法:
- 将TL路线图应用于已发表的关于利托德林化物和肺的观察性研究.
- 制定替代因果关系问题,以解决数据限制的问题.
- 探索克服不计量混的选择.
主要成果:
- 在TL路线图中,突出了一些问题,导致对更可靠的RWE进行了修订的因果问题.
- 发现缺乏足够的数据来建立因果关系曲线.
- 根据明确的假设,估计任何利托德林与没有利托德林的效果是不可能的.
结论:
- 在临床和监管使用之前,对RWE质量和可靠性的系统评估至关重要.
- 该TL路线图为彻底,透明和现实的RWE评估提供了一个框架.
- 建议在决策过程中定期实施TL路线图.
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