论文标题
评估分布危险模型在离散时间内的校准
Assessing the Calibration of Subdistribution Hazard Models in Discrete Time
论文作者
论文摘要
风险预测模型的概括性能可以通过其校准来评估，该模型衡量了预测和观察到的外部验证数据结果之间的一致性。在这里，提出了在存在竞争风险的情况下评估离散时间到事件模型的校准的方法。该方法是为离散分布危险模型类设计的，该模型将一个感兴趣的事件的累积发生率函数与一组协变量联系起来。仿真研究表明，即使在较高的审查率和/或大量离散时间点的情况下，这些方法也是用于校准评估的强大工具。通过对医院肺炎的分析来说明所提出的方法。
The generalization performance of a risk prediction model can be evaluated by its calibration, which measures the agreement between predicted and observed outcomes on external validation data. Here, methods for assessing the calibration of discrete time-to-event models in the presence of competing risks are proposed. The methods are designed for the class of discrete subdistribution hazard models, which directly relate the cumulative incidence function of one event of interest to a set of covariates. Simulation studies show that the methods are strong tools for calibration assessment even in scenarios with a high censoring rate and/or a large number of discrete time points. The proposed approaches are illustrated by an analysis of nosocomial pneumonia.
