我们先前提出了时间校准来解释随时间变化的生存趋势，以改善对新患者的预后模型预测的校准。这包括首先使用全部数据集估计预测因素效应，然后使用最近数据子集重新估计基线，同时限制预测因素效应保持不变。在本文中，我们展示了如何在竞争风险设置中应用时间校准，通过单独对每个特定原因（或亚分布）的危险模型进行重新校准。我们以结肠癌存活率的例子说明了这一点，使用了来自《监测流行病学与末期结果》（SEER）计划的数据。使用1995-2004年诊断的患者数据，拟合了针对结肠癌死亡和其他原因死亡的两个模型。我们讨论了在应用时间校准时需要考虑的因素，例如在重新校准步骤中使用的数据选择。我们还展示了如何评估这些模型在之后2005年诊断的新患者数据中的校准情况。与标准分析方法（不考虑随时间改进）和周期分析进行了比较，周期分析类似于时间校准，但在估计预测因素效应时使用的数据有所不同。10年的校准图表显示，使用标准方法会高估结肠癌死亡风险和总死亡风险，而使用时间校准或周期分析可以改善校准情况。© 2023 作者。《统计医学》由约翰·威利和儿子有限公司出版。

We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re-estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause-specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995-2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10-year calibration plots demonstrated that using the standard approach over-estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis.© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.