Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. (29th September 2020)

• Record Type: Journal Article
• Authors: Li, Ruilin; Chang, Christopher; Justesen, Johanne M; Tanigawa, Yosuke; Qian, Junyang; Hastie, Trevor; Rivas, Manuel A; Tibshirani, Robert
• Is Part Of: Biostatistics. Volume 23:Number 2(2022)
• Page Start: 522
• Page End: 540
• Publication Date: 2020-09-29
• Subjects: Concordance index -- Cox proportional hazard model -- LASSO -- Time-to-event data -- UK Biobank; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxaa038
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: 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.