Open AccessFast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank
Author(s) -
Ruilin Li,
Chris Chang,
Johanne Marie Justesen,
Yosuke Tanigawa,
Junyang Qiang,
Trevor Hastie,
Manuel A. Rivas,
Robert Tibshirani
Publication year - 2020
Publication title -
biostatistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.493
H-Index - 82
eISSN - 1468-4357
pISSN - 1465-4644
DOI - 10.1093/biostatistics/kxaa038
Subject(s) - lasso (programming language) , biobank , computer science , proportional hazards model , data mining , deviance (statistics) , scalability , statistics , algorithm , mathematics , machine learning , bioinformatics , database , world wide web , biology
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.