竞争风险数据的两个数据生成过程对两种竞争风险回归模型的歧视和校准的影响
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Peter C Austin1,2,3, Hein Putter4
1ICES, Toronto, Ontario, Canada.
Statistics in medicine
|February 27, 2026
概括
为竞争性风险模拟选择正确的数据生成过程至关重要. 不一致性对歧视指标的影响最小,但对校准准确性很重要.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 蒙特卡洛模拟在统计研究中至关重要,特别是在具有竞争风险的生存分析中.
- 相互竞争的风险是防止主要事件发生的事件.
- 模拟竞争性风险数据的现有方法依赖于因果特异性危险或分发性危险.
研究的目的:
- 调查不同数据生成过程对竞争风险场景中的预测模型性能的影响.
- 评估数据生成过程的选择如何影响歧视 (时间依赖的AUC) 和准确性 (时间依赖的Brier分数).
- 根据竞争性风险回归选择,评估对校准指标 (ICI,E50,E90) 的影响.
主要方法:
- 利用蒙特卡洛模拟来生成使用两个不同的过程的竞争性风险数据.
- 使用时间依赖的曲线下面面积 (AUC) 进行歧视来评估预测模型的性能.
- 评估精度与时间依赖的布里尔分数和校准使用集成校准指数 (ICI),E50和E90.
主要成果:
- 数据生成过程和拟合模型之间的不一致性对时间依赖的AUC和时间依赖的Brier分数的影响是最小的.
- 校准指标 (ICI,E50,E90) 对用于平滑事件概率的回归模型选择敏感.
- 对于准确的校准评估,建议配套模型与数据生成过程之间的一致性.
结论:
- 为了评估竞争风险模型中的歧视,选择数据生成过程的影响有限.
- 准确的校准评估需要使用与正在评估的模型类型相匹配的平滑事件概率模型.
- 研究人员应该仔细考虑数据生成过程和模型一致性,以获得可靠的生存分析预测.
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