@article{e22f4f2f49a34028b2311ca3d5721f68,
title = "Sampling-based estimation for massive survival data with additive hazards model",
abstract = "For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample-based estimator given the full data. The optimal subsampling probabilities are obtained via minimizing asymptotic variance of the resulting estimator. The subsample-based procedure can largely reduce the computational cost compared with the full data method. In numerical simulations, our method has low bias and satisfactory coverage probabilities. We provide an illustrative example on the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program.",
keywords = "additive hazards model, big data, subsample-based estimator, subsampling probabilities, survival analysis",
author = "Lulu Zuo and Haixiang Zhang and Wang, {Hai Ying} and Lei Liu",
note = "Funding Information: Foundation for the National Institutes of Health, NIH UL1 TR002345; National Science Foundation, USA grant DMS‐1812013 Funding information Funding Information: The authors would like to thank the Editor, the Associate Editor and two reviewers for their constructive and insightful comments that greatly improved the article. We also thank the SEER Program for permitting our access to the lymphoma cancer data. The work of Wang was supported by National Science Foundation (NSF), USA grant DMS‐1812013. The work of Liu was supported by NIH UL1 TR002345. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NSF and NIH. Publisher Copyright: {\textcopyright} 2020 John Wiley & Sons Ltd",
year = "2021",
month = jan,
day = "30",
doi = "10.1002/sim.8783",
language = "English",
volume = "40",
pages = "441--450",
journal = "Statistics in Medicine",
issn = "0277-6715",
number = "2",
}