根据发表的p值,估计医学期刊中 (随机) 临床试验的错误发现风险
Ulrich Schimmack1, František Bartoš2,3
1Department of Psychology, University of Toronto Mississauga, Mississauga, Canada.
PloS one
|August 30, 2023
概括
大多数发表的科学结果不是错误的,但按照标准标准存在13%的错误阳性风险. 降低显著性值可以降低这种风险,尽管出版偏差仍然是一个问题.
科学领域:
- 医学研究诚信 医学研究诚信
- 生物统计学 生物统计学
- 出版偏见 出版偏见
背景情况:
- 关于科学文献可信度的担忧被声称大多数公布的结果是假的所加剧.
- 之前对虚假阳性结果率的经验研究并没有最终解决这个问题.
研究的目的:
- 提出和应用一种新的方法来估计已发表的研究中的假阳性风险.
- 在领先的医学期刊上发表的随机临床试验中实证评估假阳性风险.
主要方法:
- 开发一种新的统计方法来量化假阳性风险.
- 该方法应用于来自顶级医学期刊的临床试验结果数据集.
- 分析传统显著性值 (例如,alpha = 0.05) 对虚假阳性率的影响.
主要成果:
- 与普遍的说法相反,这项研究发现,传统的显著水平alpha = 0.05产生13%的错误阳性风险.
- 将显著性值调整为alpha = 0.01,将虚假阳性风险大幅降低至5%以下.
- 该研究发现了公布偏见的明确证据,这有助于对效应大小的膨胀估计.
结论:
- 这些发现挑战了大多数公布的研究结果都是错误的.
- 修订后的显著性值 (alpha = 0.01) 为减少医学研究中错误阳性结果提供了更可靠的标准.
- 解决出版偏见对于准确的效果大小估计和提高医学研究结果的整体可信度至关重要.
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