ArticleBiostatistics (Oxford, England)2013
Structured variable selection with q-values.
Article in Biostatistics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- svReg: Structural varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups.Biometrical journal. Biometrische Zeitschrift · 2021Article
- Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.BMC medical genomics · 2018Article
- COX REGRESSION WITH EXCLUSION FREQUENCY-BASED WEIGHTS TO IDENTIFY NEUROIMAGING MARKERS RELEVANT TO HUNTINGTON'S DISEASE ONSET.The annals of applied statistics · 2016Article
- Associations between dairy foods, diabetes, and metabolic health: potential mechanisms and future directions.Metabolism: clinical and experimental · 2014Review
- Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data.Bioinformatics (Oxford, England) · 2014Article
- A diet containing a nonfat dry milk matrix significantly alters systemic oxylipins and the endocannabinoid 2-arachidonoylglycerol (2-AG) in diet-induced obese mice.Nutrition & metabolism · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
When some of the regressors can act on both the response and other explanatory variables, the already challenging problem of selecting variables when the number of covariates exceeds the sample size becomes more difficult. A motivating example is a metabolic study in mice that has diet groups and gut microbial percentages that may affect changes in multiple phenotypes related to body weight regulation. The data have more variables than observations and diet is known to act directly on the phenotypes as well as on some or potentially all of the microbial percentages. Interest lies in determining which gut microflora influence the phenotypes while accounting for the direct relationship between diet and the other variables A new methodology for variable selection in this context is presented that links the concept of q-values from multiple hypothesis testing to the recently developed weighted Lasso.
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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.