Abstract Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time‐to‐event outcomes, but the literature on validation methods for discrete time‐to‐event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time‐to‐event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator‐associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.

中文翻译：

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在存在竞争风险的情况下验证离散时间到事件预测模型

摘要 临床预测模型在风险分层、治疗分配和医疗决策的许多其他领域中发挥着关键作用。在它们进入临床实践之前，必须使用系统验证来证明它们的有用性。已经针对连续、二元和时间事件结果提出了评估其预测性能的方法，但关于具有竞争风险的离散时间事件模型的验证方法的文献很少。本文试图填补这一空白，并提出了在存在竞争风险的情况下量化离散时间到事件结果的区分、校准和预测误差 (PE) 的新方法。在我们的案例研究中，目标是预测重症监护病房 (ICU) 中铜绿假单胞菌引起的呼吸机相关性肺炎 (VAP) 的风险。竞争事件是拔管、死亡和其他细菌引起的 VAP。此应用程序的目的是验证在先前工作中开发的复杂预测模型，该模型基于最近可用的验证数据。