Wu Jing, Chen Ming-Hui, Schifano Elizabeth D, Yan Jun
Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, U.S.A.
Department of Statistics, University of Connecticut, Storrs, CT, U.S.A.
J Comput Graph Stat. 2021;30(4):1209-1223. doi: 10.1080/10618600.2020.1870481. Epub 2021 Mar 8.
When large amounts of survival data arrive in streams, conventional estimation methods become computationally infeasible since they require access to all observations at each accumulation point. We develop online updating methods for carrying out survival analysis under the Cox proportional hazards model in an online-update framework. Our methods are also applicable with time-dependent covariates. Specifically, we propose online-updating estimators as well as their standard errors for both the regression coefficients and the baseline hazard function. Extensive simulation studies are conducted to investigate the empirical performance of the proposed estimators. A large colon cancer data set from the Surveillance, Epidemiology, and End Results (SEER) program and a large venture capital (VC) data set with time-dependent covariates are analyzed to demonstrate the utility of the proposed methodologies.
当大量生存数据以流的形式到达时，传统的估计方法在计算上变得不可行，因为它们需要在每个累积点访问所有观测值。我们开发了在线更新方法，以便在在线更新框架下的Cox比例风险模型中进行生存分析。我们的方法也适用于随时间变化的协变量。具体而言，我们针对回归系数和基线风险函数提出了在线更新估计量及其标准误差。进行了广泛的模拟研究以调查所提出估计量的实证性能。分析了来自监测、流行病学和最终结果（SEER）计划的一个大型结肠癌数据集以及一个具有随时间变化协变量的大型风险投资（VC）数据集，以证明所提出方法的实用性。
