设计用于预测治疗效果的模型的性能指标.
C C H M Maas1, D M Kent2, M C Hughes2
1Department of Public Health, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands. c.h.m.maas@erasmusmc.nl.
BMC medical research methodology
|July 8, 2023
概括
新的指标评估随机临床试验 (RCT) 中的个性化治疗效果模型. 这些指标评估校准和整体性能,解决预测治疗效果现有方法的局限性.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 机器学习 机器学习
背景情况:
- 预测个性化治疗效应是复杂的,因为无法观察到的反事实结果.
- 现有的C-for-benefit指标衡量了区分能力,但缺乏校准和整体绩效指标.
- 需要强大的指标来评估临床研究中的治疗效果预测模型.
研究的目的:
- 为评估预测个性化治疗效果的模型的校准和整体性能提出新的指标.
- 扩大治疗效果模型的评估范围,超越歧视能力.
- 为在随机临床试验 (RCT) 中提供更准确的模型评估工具.
主要方法:
- 根据Mahalanobis距离,使用匹配的未接受治疗和接受治疗的患者来确定观察到的双对治疗效应.
- 引入了E-for-benefit指标 (E_avg,E_50,E_90) 来量化对平滑观察效应的预测准确性.
- 开发了交叉利和利指标来评估预测错误.
- 通过模拟验证的指标,比较"最佳"和"扰乱"模型.
- 在使用多种建模方法 (风险建模,效果建模,因果森林) 对糖尿病预防计划数据应用指标.
主要成果:
- 在所有拟议的指标中",乱模型"始终显示出比"最佳模型"更差的性能指标.
- 模拟结果表明了新指标对模型性能差异的敏感性.
- 案例研究显示,不同的建模方法的校准,区分能力和整体性能相似.
- 拟议的指标是在R包"HTEPredictionMetrics"中实施的.
结论:
- 新提出的指标有效地评估了RCT治疗效果预测模型的校准和整体性能.
- 这些指标为评估临床预测模型的工具包提供了有价值的补充.
- 随着R-Package的可用性,这些性能指标的实际应用变得更加容易.
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