Validation of prediction models in the presence of competing risks: a guide through modern methods
Validation of prediction models in the presence of competing risks: a guide through modern methods
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DOI：
10.1136/bmj-2021-069249
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发表时间：
2022-05-24
影响因子：
105.7
通讯作者：
Steyerberg, Ewout
中科院分区：
文献类型：
--
作者：
van Geloven, Nan;Giardiello, Daniele;Steyerberg, Ewout
对于任何预测模型来说，在提倡将其用于医疗实践之前，彻底的验证都是至关重要的。对于乳腺癌复发等事件发生时间结果，其他原因导致的死亡是一个相互竞争的风险。模型性能测量必须考虑此类竞争事件。在本文中，我们对这一竞争事件设置的表现测量进行了全面且易于理解的概述，包括通过决策曲线分析对校准、区分、总体预测误差和临床有用性的统计测量进行计算和解释。所有方法均针对乳腺癌患者进行说明，并提供公开数据和 R 代码。
Thorough validation is pivotal for any prediction model before it can be advocated for use in medical practice. For time-to-event outcomes such as breast cancer recurrence, death from other causes is a competing risk. Model performance measures must account for such competing events. In this article, we present a comprehensive yet accessible overview of performance measures for this competing event setting, including the calculation and interpretation of statistical measures for calibration, discrimination, overall prediction error, and clinical usefulness by decision curve analysis. All methods are illustrated for patients with breast cancer, with publicly available data and R code.