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To ensure privacy protection and alleviate computational burden, we propose afast subsmaling procedure for the Cox model with massive survival datasets frommulti-centered, decentralized sources. The proposed estimator is computed basedon optimal subsampling probabilities that we derived and enables transmissionof subsample-based summary level statistics between different storage siteswith only one round of communication. For inference, the asymptotic propertiesof the proposed estimator were rigorously established. An extensive simulationstudy demonstrated that the proposed approach is effective. The methodology wasapplied to analyze a large dataset from the U.S. airlines.

DsubCox: A Fast Subsampling Algorithm for Cox Model with Distributed and Massive Survival Data