同时推断使用多个边际模型
Ludwig A Hothorn1, Christian Ritz2, Frank Schaarschmidt3
1Leibniz University Hannover, Hannover, Germany.
Pharmaceutical statistics
|August 21, 2024
概括
本教程介绍了低维数据的同时推断,提供调整的p值和超出平均值比较的置信区间. 它利用相关性杆作用.
科学领域:
- 生物统计学 生物统计学
- 统计推理 统计推理
- 多变量数据分析 多变量数据分析
背景情况:
- 同时推断对于统计分析中的多重比较至关重要.
- 现有的方法往往缺乏复杂的终点结构的调整p值和置信区间.
- 对关联对统计测试的影响需要仔细考虑.
研究的目的:
- 描述用于低维同时推断的单步方法.
- 为各种比较提供调整的p值和置信区间.
- 为了证明多重边际模型 (mmm) 方法的应用.
主要方法:
- 利用对应关系对多变量t分布量的影响.
- 通过多重边际模型 (mmm) 方法估计相关性矩阵.
- 在使用R包的真实数据场景中使用mmm的maxT测试.
主要成果:
- 该方法支持对不同尺度,相关的多个终点进行分析.
- 它允许对相关的二进制终点进行联合分析.
- 应用包括剂量建模,剂量/时间的联合测试,子组和各种回归模型.
结论:
- 描述的同时推理方法是多功能和适用于复杂的数据结构.
- 它为多个相关的终点提供了强大的统计推断.
- 多重边际模型方法为先进的统计分析提供了灵活的框架.
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