ArticleBiostatistics (Oxford, England)2022
Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- GeneSIS: enhancing transferability of polygenic scores with variant-level gene-by-sex interaction effects.bioRxiv : the preprint server for biology · 2026Article
- Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?Biological psychiatry · 2026Review
- AI-driven network pharmacology and multi-omics validation identify KCNH2 as a prognostic biomarker and candidate therapeutic vulnerability of Acorus tatarinowii in glioblastoma.Frontiers in pharmacology · 2026Article
- Construction and evaluation of a prognostic model for breast cancer based on aging related genes.Scientific reports · 2025Article
- Transfer learning for accelerated failure time model with microarray data.BMC bioinformatics · 2025Article
- Article
- Using pre-training and interaction modeling for ancestry-specific disease prediction using multiomics data from the UK Biobank.PloS one · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.American journal of human genetics · 2024Article
- Tear Proteins Altered in Patients with Persistent Eye Pain after Refractive Surgery: Biomarker Candidate Discovery.Journal of proteome research · 2024Article
- Power of inclusion: Enhancing polygenic prediction with admixed individuals.American journal of human genetics · 2023Article
- ADuLT: An efficient and robust time-to-event GWAS.Nature communications · 2023Article
- LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK.The annals of applied statistics · 2022Article
- Accounting for age of onset and family history improves power in genome-wide association studies.American journal of human genetics · 2022Article
- Significant sparse polygenic risk scores across 813 traits in UK Biobank.PLoS genetics · 2022Article
- A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model.Scientific reports · 2022Article
- A statistical boosting framework for polygenic risk scores based on large-scale genotype data.Frontiers in genetics · 2022Article
- Fast numerical optimization for genome sequencing data in population biobanks.Bioinformatics (Oxford, England) · 2021Article
- Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.Briefings in bioinformatics · 2021Article
- Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.Nature communications · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.