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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Zuo L,Zhang H,Wang H,Liu Ldoi
10.1002/sim.8783subject
Has Abstractpub_date
2021-01-30 00:00:00pages
441-450issue
2eissn
0277-6715issn
1097-0258journal_volume
40pub_type
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pub_type: 评论,杂志文章
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