Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank

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Standard

Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. / Li, Ruilin; Chang, Christopher; Justesen, Johanne M.; Tanigawa, Yosuke; Qiang, Junyang; Hastie, Trevor; Rivas, Manuel A; Tibshirani, Robert.

I: Biostatistics, 2020.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Li, R, Chang, C, Justesen, JM, Tanigawa, Y, Qiang, J, Hastie, T, Rivas, MA & Tibshirani, R 2020, 'Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank', Biostatistics. https://doi.org/10.1093/biostatistics/kxaa038

APA

Li, R., Chang, C., Justesen, J. M., Tanigawa, Y., Qiang, J., Hastie, T., Rivas, M. A., & Tibshirani, R. (2020). Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. Biostatistics. https://doi.org/10.1093/biostatistics/kxaa038

Vancouver

Li R, Chang C, Justesen JM, Tanigawa Y, Qiang J, Hastie T o.a. Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. Biostatistics. 2020. https://doi.org/10.1093/biostatistics/kxaa038

Author

Li, Ruilin ; Chang, Christopher ; Justesen, Johanne M. ; Tanigawa, Yosuke ; Qiang, Junyang ; Hastie, Trevor ; Rivas, Manuel A ; Tibshirani, Robert. / Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. I: Biostatistics. 2020.

Bibtex

@article{ae8811d933bb4ae3a8e8b7450febf179,

title = "Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank",

abstract = "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.",

author = "Ruilin Li and Christopher Chang and Justesen, {Johanne M.} and Yosuke Tanigawa and Junyang Qiang and Trevor Hastie and Rivas, {Manuel A} and Robert Tibshirani",

year = "2020",

doi = "10.1093/biostatistics/kxaa038",

language = "English",

journal = "Biostatistics",

issn = "1465-4644",

publisher = "Oxford University Press",

}

RIS

TY - JOUR

T1 - Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank

AU - Li, Ruilin

AU - Chang, Christopher

AU - Justesen, Johanne M.

AU - Tanigawa, Yosuke

AU - Qiang, Junyang

AU - Hastie, Trevor

AU - Rivas, Manuel A

AU - Tibshirani, Robert

PY - 2020

Y1 - 2020

N2 - 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.

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.

U2 - 10.1093/biostatistics/kxaa038

DO - 10.1093/biostatistics/kxaa038

M3 - Journal article

C2 - 32989444

JO - Biostatistics

JF - Biostatistics

SN - 1465-4644

ER -

ID: 249810844