在风险预测中使用和滥用接收器操作特征曲线
1Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Ave East, Boston, MA 02215, USA. ncook@rics.bwh.harvard.edu
Circulation
|February 21, 2007
概括
c统计 (ROC曲线下的面积) 对于诊断测试是有用的,但对于风险预测模型是不那么有用的. 准确的风险分层需要模型校准,而不仅仅是歧视,以告知临床决策.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 在诊断测试中,c统计或接收器操作特征 (ROC) 曲线下的面积被广泛使用,以评估模型在患病和没有患病的个体之间进行歧视的能力.
- 然而,在预测未来风险或将个人分为风险类别的模型中,它的实用性可能是有限的,在这些模型中,校准是至关重要的.
研究的目的:
- 评估c统计在评估风险预测模型中的局限性.
- 强调校准的重要性与歧视对准确的风险评估和临床决策的重要性.
主要方法:
- 该研究讨论了风险预测中的c统计和校准的理论基础.
- 它使用心血管风险因素的例子来说明校准对患者风险重新分类和治疗建议的影响.
主要成果:
- c统计数据可能无法充分反映风险预测模型的性能,特别是那些包含已确定的风险因素,如脂质,高血压和吸烟.
- 具有良好的校准模型可以达到c的统计值远低于理论最大值1.
- 仅仅依赖c统计数据可能会导致有价值的风险因素被排除在预测得分之外.
结论:
- 校准是风险评估模型的关键组成部分,通常比临床实用性的歧视更重要.
- 在选择模型或评估新风险因素时过度依赖c统计数据是不明智的.
- 在开发和验证风险预测模型时,未来的研究应该考虑校准和歧视.
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