对随机临床试验的p值进行新的研究
Erik van Zwet1, Andrew Gelman2,3, Sander Greenland4,5
1Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
NEJM evidence
|February 6, 2024
概括
许多临床试验高估了治疗效果,因为其统计能力较低. 重新解释P值为更好地理解试验结果提供了指南,并避免过度乐观的效果大小结论.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 基于证据的医学基于证据的医学.
背景情况:
- 从Cochrane数据库中分析了23,551个随机临床试验.
- 在实际效应的试验中确定了广泛的低统计能力.
- 观察到对治疗效果的高估和对显著/非显著结果的误解.
研究的目的:
- 用研究的参考人群重新解释P值.
- 开发一种经验指南,用于解释临床试验中的P值.
- 解决过高估值,错误的效果标志和复制失败的问题.
主要方法:
- 检查的初级疗效结果来自大量系统性审查的队列.
- 实际效应与声明效应大小的估计统计能力.
- 在参考研究群体的背景下重新解释P值的意义.
主要成果:
- 从统计学上显著的发现往往高估了真实影响.
- 不显著的结果仍然可能表明重要的影响.
- 开发了P值解释的指南,包括效应高估,错误效应标志的概率和预测能力.
结论:
- 为临床试验人员提供P值的新解释.
- 帮助研究人员避免天真的P值解释和过度乐观的效果大小.
- 这些发现与医学研究和其他受低统计能力困扰的领域有关.
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