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 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.

Automated summary

This paper presents a scalable and efficient algorithm for fitting a Cox proportional hazard model, which is demonstrated to be effective on large-scale and high-dimensional data.