Evidence map›Paper›PMID 30478713›Full record

ArticleLifetime data analysis2019

An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis.

Kevin He, Yue Wang, Xiang Zhou, Han Xu, Can Huang

Abstract read
PubMed Publisher
In one paragraph

Article in Lifetime data analysis, 2019. 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, 1420 Washington Hts, Ann Arbor, MI, 48109-2029, USA. kevinhe@umich.edu.ORCID http://orcid.org/0000-0002-8354-426X
Yue WangDepartment of Statistics, University of Michigan, 1085 South University, Ann Arbor, MI, 48109-2029, USA.
Xiang ZhouDepartment of Biostatistics, University of Michigan, 1420 Washington Hts, Ann Arbor, MI, 48109-2029, USA.
Han XuDepartment of Statistics, University of Michigan, 1085 South University, Ann Arbor, MI, 48109-2029, USA.
Can HuangDepartment of Statistics, University of Michigan, 1085 South University, Ann Arbor, MI, 48109-2029, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivated by high-dimensional genomic studies, we develop an improved procedure for adaptive Lasso in high-dimensional survival analysis. The proposed procedure effectively reduces the false discoveries while successfully maintaining the false negative proportions, which improves the existing adaptive Lasso procedures. The implementation of the proposed procedure is straightforward and it is sufficiently flexible to accommodate large-scale problems where traditional procedures are impractical. To quantify the uncertainty of variable selection and control the family-wise error rate, a multiple sample-splitting based testing algorithm is developed. The practical utility of the proposed procedure are examined through simulation studies. The methods developed are then applied to a multiple myeloma data set.

Indexed as

Regression AnalysisSurvival AnalysisAlgorithmsHumansMultiple MyelomaProportional Hazards ModelsAdaptive LassoCross-validationHigh-dimensionalVariable selection

Identifiers

PMID30478713

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.