Online Updating of Survival Analysis
Date of Original Version
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.
Publication Title, e.g., Journal
Journal of Computational and Graphical Statistics
Wu, Jing, Ming Hui Chen, Elizabeth D. Schifano, and Jun Yan. "Online Updating of Survival Analysis." Journal of Computational and Graphical Statistics (2021). doi: 10.1080/10618600.2020.1870481.