ArticleStatistics in medicine2021
Sampling-based estimation for massive survival data with additive hazards model.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Perturbation Subsampling Method for Massive Censored Data.Entropy (Basel, Switzerland) · 2026Article
- A Repeated Block Perturbation Subsampling for Large-Scale Longitudinal Data.Journal of statistical theory and practice · 2026Article
- An optimal subsampling design for large-scale Cox model with censored data.Journal of applied statistics · 2025Article
- Optimal distributed subsampling for accelerated failure time models with massive censored data.Journal of applied statistics · 2025Article
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Authors and funding
4 authors.
Funding
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.
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