DsubCox: A Fast Subsampling Algorithm for Cox Model with Distributed and Massive Survival Data
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914960931553280 |
|---|---|
| author | Zhang, Haixiang Li, Yang Wang, HaiYing |
| author_facet | Zhang, Haixiang Li, Yang Wang, HaiYing |
| contents | To ensure privacy protection and alleviate computational burden, we propose a fast subsmaling procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08208 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DsubCox: A Fast Subsampling Algorithm for Cox Model with Distributed and Massive Survival Data Zhang, Haixiang Li, Yang Wang, HaiYing Computation To ensure privacy protection and alleviate computational burden, we propose a fast subsmaling procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines. |
| title | DsubCox: A Fast Subsampling Algorithm for Cox Model with Distributed and Massive Survival Data |
| topic | Computation |
| url | https://arxiv.org/abs/2310.08208 |