DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data

Zhang Haixiang (), Li Yang and Wang HaiYing
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Zhang Haixiang: Center for Applied Mathematics and KL-AAGDM, 12605 Tianjin University , Tianjin 300072, China
Li Yang: Department of Biostatistics and Health Data Science, Indiana University School of Medicine and Richard M. Fairbanks School of Public Health, Indianapolis, IN 46202, USA
Wang HaiYing: Department of Statistics, University of Connecticut, Storrs, Mansfield, CT 06269, USA

The International Journal of Biostatistics, 2025, vol. 21, issue 1, 53-65

Abstract: 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.

Keywords: distributed learning; L-optimality criterion; massive survival data; optimal subsampling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/ijb-2024-0042

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