评估个性化治疗效果预测:基于模型的歧视和校准评估视角
J Hoogland1,2, O Efthimiou3,4, T L Nguyen5
1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Statistics in medicine
|August 1, 2024
概括
评估个性化治疗效果模型至关重要. 这项研究提出了新的验证指标,并发现基于模型的统计数据提供了更高的偏差和准确性,建议独立数据可靠的绩效评估.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学 临床流行病学
- 医疗信息学 医疗信息学
背景情况:
- 对预测个性化治疗效果 (ITE) 的兴趣日益增长,需要对模型进行强有力的评估.
- 现有的文献主要集中在ITE模型开发上,对绩效评估的指导有限.
- 潜在结果框架为定义和比较ITE估计提供了基础.
研究的目的:
- 促进对个性化治疗效应的预测模型的验证.
- 检查现有的福利歧视措施,并为ITE提出新的基于模型的指标.
- 用模拟和真实世界的临床试验数据比较拟议的验证统计的性能.
主要方法:
- 使用ITE的潜在结果框架定义了估计值.
- 检查了c-for-benefit统计数据的变化,并建议基于模型的扩展用于歧视和校准.
- 利用模拟数据和对急性缺血性中风治疗的随机试验进行评估.
- 开发了一个R软件包,用于实施拟议的验证方法.
主要成果:
- 拟议的基于模型的统计数据显示,与现有方法相比,在偏差和准确性方面表现优越.
- 重采样方法,虽然调整为乐观,但表现出很高的方差,限制了它们的准确性.
- 独立数据验证被确定为评估ITE模型性能的最佳方法.
结论:
- 基于模型的统计为验证个性化治疗效果模型提供了一种可靠的方法.
- 独立的数据验证对于准确评估ITE预测模型至关重要.
- 开发的R包有助于这些先进的验证技术的实际实施.
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