解决生物标志物评估的时间依赖歧视中的受试者异质性问题
Xinyang Jiang1, Wen Li2, Ruosha Li1
1Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Statistics in medicine
|January 30, 2024
概括
这项研究引入了一种新的方法来评估生物标志物随着时间的推移如何预测疾病,并考虑到个体患者的差异. 曲线下的协同变量特定的时间依赖区域 (AUC) 可以更准确地评估生物标志物的性能.
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
- 生物标志物研究 生物标志物研究
背景情况:
- 准确的生物标志物歧视对于疾病监测和早期检测至关重要.
- 生物标志物对时间到事件结果的准确性随着时间的推移而变化,需要时间依赖的措施,如时间依赖的接收器操作特征曲线和曲线下的面积 (AUC).
- 现有的措施不考虑受试者的异质性,限制了对共变量如何影响生物标志物的表现的理解.
研究的目的:
- 提出和开发一种新的测量方法,即以共变量为特定的时间依赖的AUC,用于对共变量进行调整的歧视.
- 为了研究共变量如何影响生物标记物的性能,在规模和效果方面.
- 提供一个统计框架,用于估计,推断和拟议措施的异面性质.
主要方法:
- 开发一个回归模型,用于共变量特定的时间依赖的AUC.
- 伪部分概率的构建用于估计和推断.
- 确定拟议的估计器的非对称性质,并提供方差估计.
主要成果:
- 拟议的共同变量特定的时间依赖的AUC方法有效评估共同变量调整的歧视.
- 回归模型成功阐明了共变量对生物标志物性能的影响.
- 模拟研究和对现实世界数据的应用 (艾滋病临床试验组175) 验证了该方法的实用性.
结论:
- 协变量特定的时间依赖的AUC是评估生物标志物预测歧视的信息工具.
- 这种方法通过结合受试者的异质性和时间依赖的效应来增强对生物标志物的表现的理解.
- 这种方法对于需要在不同患者群体中准确评估生物标志物的临床应用是有价值的.
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