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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149813502&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149813502 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Apr2021 vid: 22 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 149813502 149813502 NLM31545341 149813502 10.1093/biostatistics/kxz036 NLM31545341 149813502 ppf: 381 ppct: 21 formats: tig: atl: A fast divide-and-conquer sparse Cox regression. aug: au: Wang, Yan Hong, Chuan Palmer, Nathan Di, Qian Schwartz, Joel Kohane, Isaac Cai, Tianxi affil: Department of Environmental Health, Harvard T. H. Chan School of Public Health , 401 Park Drive West, Boston, MA, 02215, USA sug: subj: Medicare Algorithms Cox Proportional Hazards Model United States Computer Simulation Aged Regression Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Funding Source Aged: 65+ years ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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