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基于倾向的标准化,以加强对目标人群的预测模型歧视的验证和解释
Valentijn M T de Jong1,2, Jeroen Hoogland1,3, Karel G M Moons1
1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
倾向加权措施标准化预测模型对案例混合差异的歧视. 这允许在外部验证样本中公平地比较模型通用性,改善解释和指导模型更新.
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:
- 外部验证对于评估预测模型性能至关重要.
- 由于样本特征 (case-mix) 和模型通用性,解释验证结果是复杂的.
- 现有的歧视指数没有区分从案例组合到模型通用性的贡献.
研究的目的:
- 提出倾向加权的措施,将案例混合效应与外部验证中的模型通用性分开.
- 为了能够公平地比较不同样本的预测模型的区分能力.
- 为特定目标人群指导模型更新策略.
主要方法:
- 开发了倾向加权歧视指标,使用对样本成员的倾向得分.
- 开发和验证样本之间的case-mix差异的标准化指标.
- 通过12个数据集的深静脉血栓预测模型来说明方法,并进行了模拟研究.
主要成果:
- 倾向性得分标准化减少了研究之间的歧视异质性.
- 这种减少表明,案例混合差异部分解释了区分能力的变化.
- 灵活的倾向得分方法在正面性假设成立时产生了公正的歧视估计.
结论:
- 基于倾向分数的标准化有助于解释跨研究预测模型的区分能力.
- 这些方法有助于理解异质性,并为目标人群指导模型更新.
- 建议对准确的评估进行仔细的倾向性得分建模,包括非线性效应.
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