ArticleLifetime data analysis2018
Group and within-group variable selection for competing risks data.
Article in Lifetime data analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- The Cox Model With Adaptive Fused Group Bridge Penalty to Incorporate Historical Data Into the Analysis of Clinical Trials With an Application to BMT CTN 1101.Statistics in medicine · 2025Article
- Joint Screening for Ultra-High Dimensional Multi-Omics Data.Bioengineering (Basel, Switzerland) · 2024Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection.Computational statistics & data analysis · 2021Article
- Risk Factor Identification in Heterogeneous Disease Progression with L1-Regularized Multi-state Models.Journal of healthcare informatics research · 2021Article
- Scalable Algorithms for Large Competing Risks Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Nonparametric competing risks analysis using Bayesian Additive Regression Trees.Statistical methods in medical research · 2020Article
- Bi-level variable selection for case-cohort studies with group variables.Statistical methods in medical researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Variable selection in the presence of grouped variables is troublesome for competing risks data: while some recent methods deal with group selection only, simultaneous selection of both groups and within-group variables remains largely unexplored. In this context, we propose an adaptive group bridge method, enabling simultaneous selection both within and between groups, for competing risks data. The adaptive group bridge is applicable to independent and clustered data. It also allows the number of variables to diverge as the sample size increases. We show that our new method possesses excellent asymptotic properties, including variable selection consistency at group and within-group levels. We also show superior performance in simulated and real data sets over several competing approaches, including group bridge, adaptive group lasso, and AIC / BIC-based methods.
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.