由于不完整的观测结果的紧张回归,适用于长度研究
Tianchen Xu1, Kun Chen2, Gen Li3
1Bristol Myers Squibb.
The annals of applied statistics
|October 3, 2024
概括
这项研究引入了一种新的方法来分析复杂的纵向微生物群数据,即使是缺失的值. 该方法揭示了婴儿肠道微生物组发育和神经发育结果之间的联系.
科学领域:
- 生物统计学 生物统计学
- 微生物组研究 微生物组研究
- 纵向数据分析 纵向数据分析
背景情况:
- 多变量纵向数据与各块缺失的值为关联分析带来了重大挑战.
- 现有的方法经常在不完整的数据集下失败,需要新的分析方法.
研究的目的:
- 开发一种新的统计方法,将高维,纵向微生物组数据与连续结果关联起来,解决块状缺失问题.
- 为了利用基于张数的回归来分析复杂的,不完整的纵向数据.
主要方法:
- 将多变量纵向数据表示为三向张量 (样本-按特征-按时间).
- 采用一种节的标量对张差回归模型.
- 开发一种基于共差的规范化估计程序,以处理缺失的数据而不进行归算.
主要成果:
- 拟议的方法有效地处理纵向数据中块wise缺失的值.
- 实现了变量选择和时间变化的效应的平稳估计.
- 在早产婴儿肠道微生物组动态和神经发育之间发现了显著的关联.
结论:
- 标量对张差回归模型为分析不完整的多变量纵向数据提供了有效的框架.
- 该方法提供了关于微生物组动态和婴儿神经发育之间的关系的见解.
- 该方法通过在微生物组,合成和衰老研究中的应用得到了验证.
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