对多重归算选择辅助变量的策略进行比较
Rheanna M Mainzer1,2, Cattram D Nguyen1,2, John B Carlin1,3
1Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
Biometrical journal. Biometrische Zeitschrift
|January 29, 2024
概括
多重归算 (MI) 使用辅助变量来改善缺失数据的估计. 包括所有变量 (完整模型) 性能最好,但LASSO选择提供了一个实用的替代方案,减少了计算时间.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 生物统计学 生物统计学
背景情况:
- 多重归算 (MI) 是解决统计分析中缺失数据的一个关键技术.
- 辅助变量可以提高MI准确性,但选择它们是具有挑战性的.
- 现有的辅助变量数据驱动的选择策略缺乏全面的绩效评估.
研究的目的:
- 为了评估八种不同的数据驱动辅助变量选择策略的性能,用于多重归算.
- 将这些策略与完整的案例分析和完整的辅助变量模型进行比较.
- 在现实世界的案例研究中评估这些方法的实际实用性.
主要方法:
- 进行了一项模拟研究,以评估八种辅助变量选择方法,包括基于关联的,假设测试,主要组件,逐步选择,缺失信息的部分和LASSO.
- 一个完整的案例分析和一个完整的MI模型作为基准.
- 这些策略还应用于一个激励性的案例研究.
主要成果:
- 完整的模型,利用所有可用的辅助变量,在模拟研究中产生了最佳估计.
- 绝对最小收缩和选择操作员 (LASSO) 策略在变量选择方法中表现最强.
- 在案例研究中,所有MI策略都产生了类似的估计,选择方法显著减少了计算时间.
结论:
- 对于多重归算,一般建议采用包含辅助变量策略.
- 基于LASSO的辅助变量选择在完整模型无法实现时提供了可行且计算效率高的替代方案.
- 对辅助变量选择的进一步研究是有必要的,以优化MI性能.
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