测试统计和统计推理数据与信息集群大小的数据
Soyoung Kim1, Michael J Martens1, Kwang Woo Ahn1
1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Biometrical journal. Biometrische Zeitschrift
|December 17, 2024
概括
这项研究引入了新的统计测试,以确定生物医学数据中的集群大小是否会影响结果. 正确计算信息集群大小可以防止回归分析中的偏差结果.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 生物医学数据分析
背景情况:
- 聚类数据在生物医学研究中很常见.
- 集群大小可以具有信息性,这意味着结果取决于它们.
- 忽视信息集群大小会偏向回归模型 (边际和混合效应).
研究的目的:
- 开发和评估测试集群大小信息性的方法.
- 专注于边缘模型,测试方法有限.
- 建议对一般化线性,考克斯和比例次分布危险模型进行分数和沃尔德测试.
主要方法:
- 开发评分和沃尔德测试来评估集群大小的信息性.
- 使用加权估计方程进行统计推理.
- 通过对二进制和右控数据的模拟来评估测试性能.
主要成果:
- 两项拟议的测试都证明了对I型错误率的良好控制.
- 评分测试显示,对右边审查的数据的功率更高.
- 沃尔德测试通常对二进制结果表现出更高的权力.
结论:
- 建议的分数和沃尔德测试对于检查集群大小的信息性是有效的.
- 这些测试适用于一般化的线性,Cox和比例分发危险模型.
- 应用到造血细胞移植数据强调了调整信息集群大小的重要性.
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