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Measures of prediction error for survival data with longitudinal covariates.

机译:具有纵向协变量的生存数据预测误差的度量。

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Prediction of future events using longitudinally collected patient measurements is increasingly popular as technical and methodological advances allow the construction of more and more complex prognostic models. We aim to give an overview of existing approaches to measure the prediction error of such dynamic predictions and link these to a measure proposed in a preceding paper (Schoop et al.), the conditional prediction error. We present theoretical results of the conditional prediction error, especially regarding the comparison of different prediction rules and its behavior in the presence of misspecification of the link between longitudinal covariates and survival time. A simulation study investigating the performance of its estimator in finite sample sizes rounds off this paper.
机译:随着技术和方法学的进步,允许构建越来越复杂的预后模型,使用纵向收集的患者测量结果来预测未来事件越来越受欢迎。我们旨在概述测量这种动态预测的预测误差的现有方法,并将这些方法与先前论文(Schoop等人)中提出的量度条件预测误差相联系。我们介绍了条件预测误差的理论结果,尤其是在纵向协变量与生存时间之间存在关联不正确的情况下,比较了不同预测规则及其行为的比较。一项模拟研究调查了其估计器在有限样本量下的性能,从而完善了本文。

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