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根据异构度的情况,对Dunnett程序进行多重性调整

Ajit C Tamhane¹, Dong Xi²

¹Northwestern University, Evanston, Illinois, USA.

Biometrical journal. Biometrische Zeitschrift

|October 4, 2023

概括

这项研究引入了一种新的模拟方法,用于在不平等变异下准确的Dunnett程序p值. 这种新的方法有效地控制了复杂的统计比较中的家族错误率 (FWER).

科学领域:

生物统计学生物统计学
统计方法统计方法
临床试验设计临床试验设计

背景情况:

丹内特程序对于将多种治疗与对照进行比较至关重要.
由于复杂的测试统计分布,标准方法难以实现异构 (不平等的差异).
现有的在异质二次性下进行多重性调整的方法可能是不准确的,导致保守的或自由的结果.

研究的目的:

开发一种基于模拟的方法,用于计算多重度调整后的p值和在异质二次性下对Dunnett过程的关键常数.
为了准确地控制治疗与对照对比的家庭错误率 (FWER),与不平等的差异进行比较.
解决现有方法的局限性,这些方法不能正确考虑Welch-Satterthwaite测试统计的相关分母.

主要方法:

提出了一种基于模拟的新型算法,以近似相关的奇平方变量的联合分布,代表测试统计数据的分母.
这种近似用于导出准确的临界常数和多重度调整后的p值.
拟议方法在控制FWER方面的性能通过在各种异种类型场景下的模拟进行评估,并与现有方法进行比较.

主要成果:

与现有方法相比,拟议的基于模拟的方法在控制家族错误率 (FWER) 中表现出更高的准确性.
其他评估的方法被发现在FWER控制中过于保守,过于自由,或不太准确.
开发的方法提供了一种更可靠的方式来进行多次治疗比较,当差异是不平等的.

结论:

这种基于模拟的新方法为在异质二次性下进行的丹内特类比较提供了强大而准确的解决方案.
这种方法提高了临床试验和其他研究环境中的统计能力和可靠性,这些研究环境中的组差异不均.
该方法的有效性通过模拟验证,并用现实世界的数据集来说明.

关键词:

与对照组进行比较.相对相关的 χ2 一个家庭的错误率.多数性调整后的p值.多变量 t 是一个多变量 t.不平等的变量不平等的变量

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