ArticleGenomics, proteomics & bioinformatics2015
Competing risks data analysis with high-dimensional covariates: an application in bladder cancer.
Article in Genomics, proteomics & bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04677205 (Molecular Signature From Tumor to Lymph Nodes), which is not on this map. Cited by 21 papers.
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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.
Molecular Signature From Tumor to Lymph Nodes: How to Identify the Right Candidate for IIIA-N2 Lung Cancer Surgery?
Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- Variable selection methods for predicting clinical outcomes following allogeneic hematopoietic cell transplantation.Scientific reports · 2021Trial
- An evaluation of variable selection methods in competing risks with one rare event: a simulation study.BMC medical research methodology · 2026Article
- SNRPE is Associated with ERK/mTOR Signaling Activation and Reduced Autophagy to Promote Lung Adenocarcinoma Cell Proliferation.OncoTargets and therapy · 2025Article
- Assessing expression patterns of PTGR1, a potential biomarker for acylfulven sensitivity in urothelial carcinoma.Scientific reports · 2024Article
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- Alternative splicing regulation and its therapeutic potential in bladder cancer.Frontiers in oncology · 2024Review
- High-dimensional feature selection in competing risks modeling: A stable approach using a split-and-merge ensemble algorithm.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Fast Lasso-type safe screening for Fine-Gray competing risks model with ultrahigh dimensional covariates.Statistics in medicine · 2022Article
- Overexpressed SNRPB/D1/D3/E/F/G correlate with poor survival and immune infiltration in hepatocellular carcinoma.American journal of translational research · 2022Article
- SNRPD1/E/F/G Serve as Potential Prognostic Biomarkers in Lung Adenocarcinoma.Frontiers in genetics · 2022Article
- HELLS serves as a poor prognostic biomarker and its downregulation reserves the malignant phenotype in pancreatic cancer.BMC medical genomics · 2021Article
- Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data.Computational and mathematical methods in medicine · 2021Article
- Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method.Frontiers in cell and developmental biology · 2021Article
- Prostaglandin Reductase 1 as a Potential Therapeutic Target for Cancer Therapy.Frontiers in pharmacology · 2021Review
- High expression of eukaryotic initiation factor 3M predicts poor prognosis in colon adenocarcinoma patients.Oncology letters · 2020Article
- Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings.Computational and mathematical methods in medicine · 2020Article
- SMARCAD1 Promotes Pancreatic Cancer Cell Growth and Metastasis through Wnt/β-catenin-Mediated EMT.International journal of biological sciences · 2019Article
- Bias Due to Confounders for the Exposure-Competing Risk Relationship.Epidemiology (Cambridge, Mass.) · 2017Article
- Altered primary chromatin structures and their implications in cancer development.Cellular oncology (Dordrecht, Netherlands) · 2016Review
- High Expression of PTGR1 Promotes NSCLC Cell Growth via Positive Regulation of Cyclin-Dependent Protein Kinase Complex.BioMed research international · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+ prediction error curves. The elastic net penalization method was shown to outperform Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant (P<0.001). Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.
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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.