在过去的几十年里，人们对分析重复事件数据的兴趣有所增加。复发事件数据的风险预测模型的一个重要方面是准确地区分具有发生复发事件的不同风险的个体。尽管一致性指数（C 指数）有效地评估了回归模型对经常性事件数据的整体判别能力，但也需要局部测量来捕获回归模型随时间的动态性能。因此，在本研究中，我们提出了一种与时间相关的 C 指数度量，用于推断模型的局部判别能力。我们使用灵活的参数模型将 C 指数表示为时间的函数，并构建了基于一致性的估计和推理的可能性。我们采用扰动重采样程序进行方差估计。进行了大量的模拟来研究所提出的时间相关 C 指数的有限样本性能和估计程序。我们将时间依赖性 C 指数应用于结直肠癌患者再住院研究的三个回归模型，以评估模型的判别能力。© 作者 2023。由牛津大学出版社出版。版权所有。 [br]如需权限，请发送电子邮件至：journals.permissions@oup.com。

Interest in analyzing recurrent event data has increased over the past few decades. One essential aspect of a risk prediction model for recurrent event data is to accurately distinguish individuals with different risks of developing a recurrent event. Although the concordance index (C-index) effectively evaluates the overall discriminative ability of a regression model for recurrent event data, a local measure is also desirable to capture dynamic performance of the regression model over time. Therefore, in this study, we propose a time-dependent C-index measure for inferring the model's discriminative ability locally. We formulated the C-index as a function of time using a flexible parametric model and constructed a concordance-based likelihood for estimation and inference. We adapted a perturbation-resampling procedure for variance estimation. Extensive simulations were conducted to investigate the proposed time-dependent C-index's finite-sample performance and estimation procedure. We applied the time-dependent C-index to three regression models of a study of re-hospitalization in patients with colorectal cancer to evaluate the models' discriminative capability.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.