通过minP调整来解决研究人员自由度的问题
Maximilian M Mandl1,2, Andrea S Becker-Pennrich3,4, Ludwig C Hinske4,5
1Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, LMU Munich, Marchioninistr. 15, Munich, 81377, Germany. mmandl@ibe.med.uni-muenchen.de.
BMC medical research methodology
|July 17, 2024
概括
研究人员可以通过使用minP调整方法来减少假阳性结果,以考虑多种分析策略. 这种方法控制了研究人员的自由度,提高了科学发现的可靠性.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 医学研究方法学 医学研究方法学
背景情况:
- 研究人员在分析选择中的灵活性,称为研究人员的自由度,可以导致错误阳性和过度乐观的结果增加.
- 对有利结果的选择性报告加剧了研究人员自由度的问题.
- 像Bonferroni这样的传统的复数校正方法通常过于保守,在处理依赖测试统计时缺乏统计能力.
研究的目的:
- 将多重分析策略的多样性正式化为多重测试问题,以解决研究人员自由度的问题.
- 提出和评估minP调整方法,以控制假阳性率,同时保持统计能力.
- 为了在现实世界的神经外科研究中展示minP程序的应用.
主要方法:
- 将多种分析策略的多样性正式化为多重测试问题.
- 提出了minP调整方法,该方法使用基于排列的程序来近似最小的p值的零分布.
- 在一个神经外科研究中,应用minP程序来调整48种不同的分析策略.
主要成果:
- minP程序考虑了测试统计数据之间的依赖关系,比天真方法提供了更大的力量.
- 该方法确保对家庭智能错误率的控制较弱,降低了假阳性结果的风险.
- 应用到神经外科研究中,证明了选择性地报告最有说服力的证据的能力,同时控制了1型错误.
结论:
- minP调整方法提供了一个统计学上合理的方法来管理研究人员的自由度.
- 这种方法通过控制错误阳性来提高研究结果的可靠性和可复制性.
- 提出的方法对于确保复杂数据分析中可靠和可靠的结果是有价值的.
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