Abstract

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 dataset from the Surveillance, Epidemiology, and End Results program and a large venture capital dataset with time-dependent covariates are analyzed to demonstrate the utility of the proposed methodologies. Supplemental files for this article are available online.

摘要

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，因为它们需要访问每个累积点的所有观测值。我们开发了在线更新方法，用于在在线更新框架中的 Cox 比例风险模型下进行生存分析。我们的方法也适用于时间相关的协变量。具体来说，我们建议在线更新估计量及其回归系数和基线风险函数的标准误差。进行了广泛的模拟研究以研究所提出的估计器的经验性能。来自监测、流行病学、分析最终结果程序和具有时间相关协变量的大型风险投资数据集，以证明所提出方法的实用性。本文的补充文件可在线获取。