Department of Biostatistics at the Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben, Switzerland.
UMR 1137, IAME, University Paris-Diderot, Inserm, Paris, France.
Biom J. 2020 May;62(3):643-657. doi: 10.1002/bimj.201800293. Epub 2019 Jul 31.
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.
临床预测模型在风险分层、治疗分配和许多其他医学决策领域发挥着关键作用。在将其应用于临床实践之前，必须使用系统验证来证明其有用性。已经提出了用于连续、二分类和事件时间结局的评估其预测性能的方法，但关于具有竞争风险的离散事件时间结局模型验证方法的文献却很少。本文试图填补这一空白，并提出了一种新的方法来量化存在竞争风险时离散事件时间结局的区分度、校准度和预测误差（PE）。在我们的案例研究中，目标是预测重症监护病房（ICU）中由铜绿假单胞菌引起的呼吸机相关性肺炎（VAP）的风险。竞争事件包括拔管、死亡和由其他细菌引起的 VAP。本应用旨在对以前工作中开发的复杂预测模型进行验证，这些模型基于最近获得的验证数据。
