"蛋糕可以引起疹吗?" - 随意的二分化导致错误的阳性结果
David Robert Grimes1,2, James Heathers3,4
1TCD Biostatistics Unit, Discipline of Public Health and Primary Care, School of Medicine, Trinity College Dublin, Dublin, Ireland. davidrobert.grimes@tcd.ie.
BMC medical research methodology
|November 13, 2025
概括
使用灵活值对连续的生物医学数据进行二分化,称为"随性二分化",可以创造真实效应的错觉. 这种做法显著增加了科学研究中假阳性结果的风险.
科学领域:
- 生物统计学 生物统计学
- 医学研究方法学 医学研究方法学
- 数据分析 数据分析
背景情况:
- 连续的生物医学数据经常被二分化用于分析,这是统计学家不鼓励的做法.
- 两体化减少了统计能力,并可能掩盖重要的趋势.
- 这篇论文强调二分化是通过"放荡的二分化"来操纵数据的工具,为研究人员提供了不必要的灵活性.
研究的目的:
- 通过操纵二分法值来量化产生虚假阳性效应的概率.
- 在均分布的数据中评估工程造假发现的潜力.
- 用现实世界健康数据 (NHANES) 来说明使用值的操纵.
主要方法:
- 使用分析方法和蒙特卡洛模拟来估计虚假发现.
- 量化了从值操纵中预期的虚假阳性数量.
- 在NHANES数据中证明了工程造假关系,例如血糖和疹状况.
主要成果:
- 即使小样本大小 ([公式:见文本]) 也可以产生[公式:见文本]的假阳性率.
- 较大的样本显示高的假阳性率 ([公式:见文本]和[公式:见文本]) 即使低数排除.
- 值操纵是一种可行的方法,可以在大多数配置中创建假阳性结果.
结论:
- 操纵切断点是发表科学数据操纵的重要来源.
- 随着大型健康数据库的日益普及,这一问题变得更加严重.
- 讨论了识别潜在的乱交二分化的含义和方法.
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