逻辑回归与预测平均值匹配用于赋值二进制共变量
Peter C Austin1,2,3, Stef van Buuren4,5
1ICES, Toronto, ON, Canada.
Statistical methods in medical research
|September 26, 2023
概括
预测平均值匹配为逻辑回归提供了一个更快的替代方案,用于在多变量推算中使用链式方程 (MICE) 模拟来赋值缺失的二进制数据,并具有可比的统计性能.
科学领域:
- 统计 统计 统计 统计
- 计算统计学 计算统计学
- 数据科学数据科学数据科学
背景情况:
- 使用链式方程 (MICE) 的多变量推算是处理缺失数据的常用方法.
- 参数归算 (逻辑回归) 是对二进制变量的标准,但预测平均值匹配在R中更快.
- 对二进制变量归算的预测平均值匹配的统计性能存在有限的研究.
研究的目的:
- 为了比较预测平均值匹配与逻辑回归的统计性能,用于赋值缺失的二进制变量.
- 在不同的样本大小和缺失数据流行情况下评估这些方法.
主要方法:
- 使用蒙特卡洛模拟来评估性能.
- 感兴趣的分析模型是多变量逻辑回归.
- 模拟不同样本大小 (N=250到10,000) 和缺失数据的流行率 (5%到50%).
主要成果:
- 预测平均值匹配证明了与二进制变量归算的逻辑回归几乎相同的统计性能.
- 这种等价性适用于各种样本大小和缺失数据百分比.
- 预测平均值匹配在模拟中显著减少计算时间.
结论:
- 预测平均值匹配是一种统计学上合理的方法,用于赋值缺失的二进制变量.
- 它为多个归算模拟提供了实质性的计算效率增长.
- 研究人员可以自信地使用预测平均值匹配来计算二进制数据,特别是当速度是一个问题时.
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