在多重等价性测试中改进了对家族智能的错误率控制
Gwenaël G R Leday1, Jesse Hemerik1, Jasper Engel1
1Wageningen University and Research, Biometris, Droevendaalsesteeg 1, 6708, PB, Wageningen, the Netherlands.
概括
在测试许多特征时,食品安全等价性测试可能会导致错误的阳性结果. 像Adaptive Bonferroni这样的新方法比霍赫伯格的方法提供了比霍赫伯格的方法更强大的家庭错误率 (FWER) 控制,提高了安全评估的准确性.
科学领域:
- 农业科学 农业科学
- 统计科学 统计科学
- 食品安全科学 食品安全科学
背景情况:
- 对于食品和料安全性评估来说,等价性测试至关重要,通过证明作物特征的等价性,使得市场批准成为可能.
- 当前每种分析物应用的单变量等价性测试缺乏多重性校正,在评估多个特征时增加I型错误率 (虚假的等价性声明).
研究的目的:
- 评估和比较不同家庭错误率 (FWER) 控制方法在食品安全等效性测试中的功率.
- 为了识别更强大的替代方案,霍赫伯格的方法管理在等价性评估多重比较.
主要方法:
- 将霍赫伯格的方法与其他FWER控制程序进行比较,包括霍梅尔的方法和自适应的邦费罗尼方法.
- 将这些方法应用于两个现实世界的组成数据集.
- 使用模拟数据进行评估和比较,以评估各种场景下的性能.
主要成果:
- 霍梅尔的方法被证明至少和霍赫伯格的方法一样强大.
- 适应性Bonferroni方法,利用非等效特征的估计器,经常显示出比Hommel方法更大的实力.
- 适应性Bonferroni方法在食品安全环境中特别有利,因为预计会有很高比例的真实等价值.
结论:
- 在食品安全方面,标准的同等性测试实践可能由于未经纠正的多重性而过于保守.
- 适应性Bonferroni和Hommel的方法在多分析剂等效性测试中为FWER控制提供了更好的统计能力.
- 这些先进的方法提高了对新食品和料产品的安全评估的准确性和效率.
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