ArticleStatistics in medicine2014
Variable selection in subdistribution hazard frailty models with competing risks data.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- An evaluation of variable selection methods in competing risks with one rare event: a simulation study.BMC medical research methodology · 2026Article
- Identification of Risk Factors for Relapse in Childhood Leukemia Using Penalized Semi-parametric Mixture Cure Competing Risks Model.Journal of research in health sciences · 2024Article
- Association of frailty with the use of health services in older adults: a secondary analysis of the Mexican Health and Aging Study.Gaceta medica de Mexico · 2024Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- High-dimensional feature selection in competing risks modeling: A stable approach using a split-and-merge ensemble algorithm.Biometrical journal. Biometrische Zeitschrift · 2023Article
- On correlation rank screening for ultra-high dimensional competing risks data.Journal of applied statistics · 2022Article
- Pretransplant survival of patients with end-stage heart failure under competing risks.PloS one · 2022Article
- 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
- Competing Risk Analyses of Medullary Carcinoma of Breast in Comparison to Infiltrating Ductal Carcinoma.Scientific reports · 2020Article
- Article
- Group and within-group variable selection for competing risks data.Lifetime data analysis · 2018Article
- Penalized variable selection in competing risks regression.Lifetime data analysis · 2017Article
- Bias Due to Confounders for the Exposure-Competing Risk Relationship.Epidemiology (Cambridge, Mass.) · 2017Article
- Penalized Variable Selection for Joint AFT Random-Effect Model With Clustered Competing-Risks Data.Pharmaceutical statisticsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The proportional subdistribution hazards model (i.e. Fine-Gray model) has been widely used for analyzing univariate competing risks data. Recently, this model has been extended to clustered competing risks data via frailty. To the best of our knowledge, however, there has been no literature on variable selection method for such competing risks frailty models. In this paper, we propose a simple but unified procedure via a penalized h-likelihood (HL) for variable selection of fixed effects in a general class of subdistribution hazard frailty models, in which random effects may be shared or correlated. We consider three penalty functions, least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and HL, in our variable selection procedure. We show that the proposed method can be easily implemented using a slight modification to existing h-likelihood estimation approaches. Numerical studies demonstrate that the proposed procedure using the HL penalty performs well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The usefulness of the new method is illustrated using two actual datasets from multi-center clinical trials.
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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.