@article {137, title = {Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression.}, journal = {Bioinformatics}, year = {2021}, month = {2021 Feb 09}, abstract = {

MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data.

RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020).

AVAILABILITY: https://github.com/rivas-lab/multisnpnet-Cox.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btab095}, author = {Li, Ruilin and Tanigawa, Yosuke and Justesen, Johanne M and Taylor, Jonathan and Hastie, Trevor and Tibshirani, Robert and Rivas, Manuel A} } @article {119, title = {Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.}, journal = {Biostatistics}, year = {2020}, month = {2020 Sep 29}, 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.

}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxaa038}, author = {Li, Ruilin and Chang, Christopher and Justesen, Johanne M and Tanigawa, Yosuke and Qiang, Junyang and Hastie, Trevor and Rivas, Manuel A and Tibshirani, Robert} }