使用ROC曲线分析进行预测给出了错误的结果:使用基于预测性的指数
A Indrayan1, R K Malhotra2, M Pawar1
1Department of Clinical Research, Max Healthcare, New Delhi, India.
Journal of postgraduate medicine
|April 26, 2024
概括
传统的ROC曲线和尤登指数对模型预测具有误导性. 新的基于预测性的ROC曲线和P指数提供了对不同种群发病率的预测功效的准确评估.
科学领域:
- 生物统计学 生物统计学
- 预测建模预测建模
- 医疗信息学 医疗信息学
背景情况:
- 接收器运行特征 (ROC) 曲线和尤登指数是评估预测模型的标准指标.
- 敏感性和特异性是ROC曲线的基础,是追溯的,不考虑患病率,导致误导性预测.
研究的目的:
- 为了证明使用ROC曲线和Youden指数进行预测评估的谬误.
- 提出用于准确预测评估的新方法,其中包括流行率.
主要方法:
- 对敏感性和特异性的回顾性分析.
- 基于预测性的ROC曲线的开发和说明.
- 引入一个P指数以实现最佳预测切线选择.
主要成果:
- 敏感性和特异性对于预测未来事件是不够的,因为它们具有追溯性,并且没有考虑流行率.
- 基于预测性的ROC曲线准确地反映了不同流行情景的预测有效性.
- 拟议的P指数提供了一个比Youden指数更可靠的最佳切割线.
结论:
- 标准ROC曲线和尤登指数不适合用于评估预测模型.
- 推基于预测性的ROC曲线和P指数用于准确的预测评估和最佳的切线确定.
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