ArticleBiostatistics (Oxford, England)2021
A fast divide-and-conquer sparse Cox regression.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.Scientific reports · 2022Pooled it
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- Communication-efficient federated learning of temporal effects on opioid use disorder with data from distributed research networks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Optimal distributed subsampling for accelerated failure time models with massive censored data.Journal of applied statistics · 2025Article
- Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.Scientific reports · 2024Article
- Fitting the Cox proportional hazards model to big data.Biometrics · 2024Article
- An online framework for survival analysis: reframing Cox proportional hazards model for large data sets and neural networks.Biostatistics (Oxford, England) · 2023Article
- Healthcare center clustering for Cox's proportional hazards model by fusion penalty.Statistics in medicine · 2023Article
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- Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy.BMC medical informatics and decision making · 2022Article
- Default risk prediction and feature extraction using a penalized deep neural network.Statistics and computing · 2022Article
- Sampling-based estimation for massive survival data with additive hazards model.Statistics in medicine · 2021Article
- Online Updating of Survival Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Scalable Algorithms for Large Competing Risks Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Distributed Simultaneous Inference in Generalized Linear Models via Confidence Distribution.Journal of multivariate analysis · 2020Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$. The proposed algorithm achieves computational efficiency through a one-step linear approximation followed by a least square approximation to the partial likelihood (PL). These sequences of linearization enable us to maximize the PL with only a small subset and perform penalized estimation via a fast approximation to the PL. The algorithm is applicable for the analysis of both time-independent and time-dependent survival data. Simulations suggest that the proposed DAC algorithm substantially outperforms the full sample-based estimators and the existing DAC algorithm with respect to the computational speed, while it achieves similar statistical efficiency as the full sample-based estimators. The proposed algorithm was applied to extraordinarily large survival datasets for the prediction of heart failure-specific readmission within 30 days among Medicare heart failure patients.
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