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扩展DeLong算法,用于比较与缺失数据相关的接收器操作特征曲线下的区域.
Lily Zou1, Yun-Hee Choi2, Leonardo Guizzetti2
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
Statistics in medicine
|July 16, 2024
概括
用于比较接收器操作特征曲线的DeLong方法可以通过省略缺失值的数据来产生无效的结果. 本研究引入了一种新,有效和高效的方法来处理这些比较中缺少的数据.
科学领域:
- 生物统计学 生物统计学
- 统计方法 统计方法
- 医疗数据分析 医学数据分析
背景情况:
- 德朗方法 (1988) 被广泛用于比较与相关的接收机操作特征 (ROC) 曲线下的面积.
- 现有的DeLong方法的软件实现可能会产生无效或无效的结果,因为它会排除任何缺失数据的个体.
- 缺少数据是现实世界数据集中常见的问题,需要强大的统计方法.
研究的目的:
- 解决DeLong方法关于缺少数据的局限性.
- 开发一种简化和更强大的算法,用于比较相关ROC曲线下的面积.
- 为处理多变量ROC分析中缺少数据提供有效和有效的方法.
主要方法:
- 开发了使用等级的DeLong算法的简化版本.
- 采用混合模型方法来扩展算法用于缺失值的多变量数据.
- 拟议的程序通过模拟研究来验证随机缺失的数据.
主要成果:
- 模拟结果证实了拟议程序的有效性和效率.
- 新方法有效地适应了缺少的数据,克服了原始DeLong方法的局限性.
- 与标准实现相比,该程序在处理含有缺失值的数据集方面表现出卓越的性能.
结论:
- 拟议的混合模型方法为与缺少数据相关的ROC曲线下的区域进行比较提供了有效和高效的替代方案.
- 这种方法在存在不完整数据集的情况下提高了统计分析的可靠性.
- 这一过程在流行的统计软件 (SAS,Stata,R) 中得到了说明,并且可以使用.
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