A fast divide-and-conquer sparse Cox regression.
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 eff...
| Publicado en: | Biostatistics Vol. 22; no. 2; pp. 381 - 402 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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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