ArticleComputational statistics & data analysis2019
Covariance-Insured Screening.
Article in Computational statistics & data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- A Group Structure Guided Ultra-High Dimensional Feature Screening for Survival Outcome.Statistics in medicine · 2026Article
- Multivariate Regression With Dependence Structures: Evaluating Associations Between Plasma Metabolomics and Alcohol Intake in Older Adults.Statistics in medicine · 2026Article
- An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping.Molecular and cellular neurosciences · 2023Article
- Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data.Computational statistics & data analysis · 2023Article
- Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.Statistica Sinica · 2022Article
- High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression.Bioinformatics (Oxford, England) · 2022Article
- A selective overview of feature screening methods with applications to neuroimaging data.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss weak signals. By incorporating the inter-feature dependence, a covariance-insured screening approach is proposed to identify predictors that are jointly informative but marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and a real data study for selecting potential genetic factors related to the onset of multiple myeloma.
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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.