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在使用多重归算时,评估测试的统计学意义的中位数p值方法
Peter C Austin1,2,3, Iris Eekhout4, Stef van Buuren4,5
1ICES, Toronto, Canada.
Journal of applied statistics
|April 30, 2025
概括
中位数p值方法在计算数据集的测试统计数据组合时膨胀统计学意义. 由于结果不可靠,特别是缺失数据增加,不应使用此方法.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 多重归算是统计分析中处理缺失数据的标准技术.
- 鲁宾规则被广泛用于从多次归纳数据集中汇集结果,但不适合测试统计.
- 目前用于汇集测试统计数据的方法复杂,并且在软件中没有广泛实施.
研究的目的:
- 评估中位数p值方法的性能,用于在计算样本中汇集测试统计数据.
- 确定p值中位数方法用于评估统计显著性的可靠性.
主要方法:
- 测试了p值中位数的方法,使用九种常见的统计测试,包括t测试,ANOVA,相关性和回归.
- 在不同水平的缺失数据下,为每个测试计算了经验I型错误率.
- 性能与广告中的统计学显著性水平进行了比较.
主要成果:
- 中位数p值方法导致所有测试的统计分析的膨胀的经验I型错误率.
- I型错误率的通货膨胀与缺失数据的普遍性成比例增加.
- 该方法证明了统计学意义的持续高估.
结论:
- 中位数p值方法在从归算数据集中汇集测试统计数据时,对评估统计显著性是不可靠的.
- 研究人员应避免使用中位数p值方法,因为它倾向于产生假阳性.
- 需要使用替代的,更强大的方法来将测试统计数据集中到多重归算数据中.
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