对多中心研究的归算策略的评估:应用于大型临床病理学数据集
Lucy Grigoroff1, Reika Masuda1, John Lindon2
1Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Australia.
PloS one
|November 20, 2025
概括
missForest是一种随机森林归算方法,有效地处理大型数据集中缺失的数据,性能优于MICE. 建议在归算之前存储和共享原始数据,以进行可靠的分析.
科学领域:
- 数据科学和生物信息学
- 统计建模 统计建模
- 临床前研究 临床前研究
背景情况:
- 从一个大规模的多站点临床前纵向病理学研究中利用了3817个完整的临床化学病例.
- 人工引入了三种不同比例的缺失数据类型,以模拟现实世界的场景.
研究的目的:
- 评估两个基于随机森林 (RF) 的归算方法: missForest 和 MICE.
- 评估变量包含和分层策略对归算性能的影响.
- 确定最佳策略来处理大,异质的临床前数据集中缺少的数据.
主要方法:
- 在大型临床前数据集上比较 missForest 和 MICE 归算技术.
- 引入了人工缺失,以评估各种条件下的强度和性能.
- 评估了分层对两种方法的归算精度的影响.
主要成果:
- 与MICE相比,missForest表现出更高的性能,显示出强度和自动变量选择能力.
- 分层策略对 missForest 的表现产生了微不足道的影响.
- 分层显著降低了MICE归算方法的性能.
结论:
- missForest是一种高效的方法,用于在大型复杂数据集中归纳缺失的数据.
- 在归算之前存储和共享原始数据集是全面数据分析的推做法.
- 在合并的数据集上可以有效地执行推算,提高数据完整性.
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