The Cox model is the most popular tool for analyzing time-to-event data. The nonparametric baseline hazard function can be as important as the regression coefficients in practice, especially when prediction is needed. In the context of stochastic process control, we propose a simultaneous monitoring method that combines a multivariate control chart for the regression coefficients and a profile control chart for the cumulative baseline hazard function that allows for data blocks of possibly different censoring rates and sample sizes. The method can detect changes in either the parametric or the nonparametric part of the Cox model. In simulation studies, the proposed method maintains its size and has substantial power in detecting changes in either part of the Cox model. An application in lymphoma survival analysis in which patients were enrolled by 2-month intervals in the Surveillance, Epidemiology, and End Results program identifies data blocks with structural model changes.

中文翻译：

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在事件时间数据的 Cox 建模中同时监测回归系数和基线危险概况。

Cox 模型是最流行的用于分析事件时间数据的工具。在实践中，非参数基线风险函数可能与回归系数一样重要，尤其是在需要预测时。在随机过程控制的背景下，我们提出了一种同时监测方法，该方法结合了回归系数的多元控制图和累积基线风险函数的剖面控制图，允许数据块的审查率和样本大小可能不同。该方法可以检测 Cox 模型的参数或非参数部分的变化。在模拟研究中，所提出的方法保持其大小，并且在检测 Cox 模型任一部分的变化方面具有强大的能力。