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, we propose methods for assessing the calibration of discrete time-to-event models in the presence of competing risks. Specifically, we consider the class of discrete subdistribution hazard models, which directly relate the cumulative incidence function of one event of interest to a set of covariates. We apply the methods to a prediction model for the development of nosocomial pneumonia. 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.

中文翻译：

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在离散时间评估子分布风险模型的校准

风险预测模型的泛化性能可以通过其校准来评估，校准衡量外部验证数据的预测结果和观察结果之间的一致性。在这里，我们提出了在存在竞争风险的情况下评估离散事件时间模型校准的方法。具体来说，我们考虑离散子分布风险模型的类别，它直接将一个感兴趣事件的累积发生率函数与一组协变量联系起来。我们将这些方法应用于医院获得性肺炎发展的预测模型。模拟研究表明，即使在具有高审查率和/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。