不同的归算方法对估计和模型性能的影响:使用早期死亡风险预测模型的一个例子
Mackenzie Hurst1,2, Meghan O'Neill1, Lief Pagalan1,3
1Population Health Analytics Lab, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Population health metrics
|June 17, 2024
概括
模式归算在人口健康调查中的预测性能方面是有效的,而多重归算在无偏的危险比率方面更优越. 选择正确的归算方法对于开发缺乏数据的准确预测模型至关重要.
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:
- 缺少数据是开发预测模型来预先死亡的一个常见挑战.
- 计算方法的选择可以显著影响模型性能和估计.
- 了解不同归算技术的性能对于可靠的健康预测至关重要.
研究的目的:
- 为了比较各种归算方法对预期早死亡率预测模型估计和性能的影响.
- 评估完整案例,模式,单项和多项归算如何影响预测准确性,区分和校准.
主要方法:
- 针对性别的韦布尔加速失效时间生存模型应用于四个数据集.
- 缺少的值使用完整案例,模式,单个和多个归算技术进行了归算.
- 六个性能指标 (Nagelkerke R2,Brier分数,Harrell的c指数,歧视斜率,大校准,校准斜率) 用于比较.
主要成果:
- 失踪比例高达10.86% (女性) 和8.24% (男性).
- 模式归算显示了Nagelkerke R2和Harrell的c指数的竞争性结果,类似于单个和多个归算.
- 在各种归算方法中观察到性能指标的小差异,这表明在这种情况下的稳定性.
结论:
- 当预测性表现是人口健康调查的首要目标时,模式归因是一种可行的选择.
- 多重归算方法对于获得无偏的危险比率是优越的.
- 归算策略的选择应以分析的具体目标和缺失数据的性质为指导.
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