Evidence map›Paper›PMID 30864387›Full record

ArticleStatistical applications in genetics and molecular biology2018

False discovery control for penalized variable selections with high-dimensional covariates.

Kevin He, Xiang Zhou, Hui Jiang, Xiaoquan Wen, Yi Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistical applications in genetics and molecular biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kevin HeDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Xiang ZhouDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Hui JiangDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Xiaoquan WenDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Yi LiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Tissue/InformaticsP50CA186786 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Robert Timothy Dess · 2014 to 2026
$27.6M
The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
NCI NIH HHS P50 CA186786NCI NIH HHS U01 CA209414
6 · The paper itself

Abstract

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors much exceeding the sample size. Penalized variable selection has emerged as a powerful and efficient dimension reduction tool. However, control of false discoveries (i.e. inclusion of irrelevant variables) for penalized high-dimensional variable selection presents serious challenges. To effectively control the fraction of false discoveries for penalized variable selections, we propose a false discovery controlling procedure. The proposed method is general and flexible, and can work with a broad class of variable selection algorithms, not only for linear regressions, but also for generalized linear models and survival analysis.

Indexed as

Models, StatisticalAlgorithmsComputer SimulationReproducibility of Resultsdimension reductionfalse discoverypenalized regressionvariable selection

Identifiers

PMID30864387
PMCPMC6450074

What OpenQuestion holds

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Registered trials

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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.