UPRES 3859, SFR 4208, HIFIH, Angers University, Angers, France.
UMR INSERM 1066, CNRS 6021, MINT, Angers University, Angers, France.
Stat Methods Med Res. 2023 Sep;32(9):1811-1822. doi: 10.1177/09622802231188521. Epub 2023 Jul 25.
With the development of personalized medicine, the study of individual prognosis appears to be a major contemporary scientific issue. Dynamic models are particularly well adapted to such studies by allowing some potential changes in the follow-up to be taken into account. In particular, this leads to more accurate predictions by updating the available information throughout the patient monitoring. Some mathematical tools have been developed to quantify and compare the effectiveness of dynamic predictions using dynamic versions of the area under the receiver operating characteristic curve and the Brier score in the competing risks setting. Nevertheless, only two predictive abilities can be compared. This may be too restrictive in a clinical context where more and more information can be collected during patient follow-up thanks to recent technological advances. Here we propose a new procedure that allows multiple comparisons of the predictive abilities of different biomarkers, based on the dynamic area under the receiver operating characteristic curve or Brier score. Performances of our testing procedure were assessed by simulations. Moreover, a motivating application in hepatology will be presented. Finally, this work compares more than two dynamic predictive abilities of biomarkers and is available via R functions on GitHub.
随着个性化医学的发展，个体预后的研究似乎是一个主要的当代科学问题。动态模型特别适合此类研究，因为它允许考虑随访中一些潜在的变化。特别是，通过在整个患者监测过程中更新可用信息，这可以实现更准确的预测。已经开发了一些数学工具，用于使用竞争风险环境下动态接收器操作特征曲线和 Brier 得分的动态版本来量化和比较动态预测的有效性。然而，只能比较两种预测能力。在临床环境中，这可能过于局限，因为最近的技术进步使得可以在患者随访期间收集越来越多的信息。在这里，我们提出了一种新的程序，该程序可以基于动态接收器操作特征曲线或 Brier 得分来比较不同生物标志物预测能力的多次比较。通过模拟评估了我们的测试程序的性能。此外，还将呈现一个在肝脏病学中的应用实例。最后，这项工作比较了生物标志物的两个以上的动态预测能力，并且可以通过 GitHub 上的 R 函数获得。
