Online Updating of Survival Analysis

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...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2021, Vol.30 (4), p.1209-1223
Main Authors: Wu, Jing, Chen, Ming-Hui, Schifano, Elizabeth D., Yan, Jun
Format: Article
Language:English
Subjects:
and end results (SEER)
Cox model
Data compression
Datasets
epidemiology
Estimators
Piecewise constant baseline hazard
Regression coefficients
Statistical models
Streaming survival data
Surveillance
Survival
Time dependence
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Summary: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.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2020.1870481