Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.
We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable...
| Publicado en: | Biostatistics Vol. 23; no. 2; pp. 522 - 541 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2022
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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=156290584&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156290584 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Apr2022 vid: 23 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 156290584 156290584 NLM32989444 156290584 10.1093/biostatistics/kxaa038 NLM32989444 156290584 ppf: 522 ppct: 19 formats: tig: atl: Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. aug: au: Li, Ruilin Chang, Christopher Justesen, Johanne M Tanigawa, Yosuke Qian, Junyang Hastie, Trevor Rivas, Manuel A Tibshirani, Robert affil: Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA 94305, USA sug: subj: Algorithms Tissue Banks Cox Proportional Hazards Model Probability Clinical Assessment Tools ab: We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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