Evidence map›Paper›PMID 37854542›Full record

ArticleStat (International Statistical Institute)2022

Regularized Buckley-James method for right-censored outcomes with block-missing multimodal covariates.

Haodong Wang, Quefeng Li, Yufeng Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Stat (International Statistical Institute), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Haodong WangDepartment of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA.
Quefeng LiDepartment of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, 27516, North Carolina, USA.
Yufeng LiuDepartment of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA.ORCID 0000-0002-1686-0545

Funding

Statistical Methods for Integrative Analysis of Large Scale Neuroimaging DataR01AG073259 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, QUEFENG · 2021 to 2025
$1.8M
Unlocking complex co-expression network using graphical modelsR01GM126550 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIU, YUFENG · 2017 to 2020
$1.6M
NIA NIH HHS R01 AG073259NIGMS NIH HHS R01 GM126550
6 · The paper itself

Abstract

High-dimensional data with censored outcomes of interest are prevalent in medical research. To analyze such data, the regularized Buckley-James estimator has been successfully applied to build accurate predictive models and conduct variable selection. In this paper, we consider the problem of parameter estimation and variable selection for the semiparametric accelerated failure time model for high-dimensional block-missing multimodal neuroimaging data with censored outcomes. We propose a penalized Buckley-James method that can simultaneously handle block-wise missing covariates and censored outcomes. This method can also perform variable selection. The proposed method is evaluated by simulations and applied to a multimodal neuroimaging dataset and obtains meaningful results.

Indexed as

accelerated failure time modelcensored datahigh dimensional datamissing covariatesmoment estimationsurvival analysis

Identifiers

PMID37854542
PMCPMC10583730

What Socratic holds

Textmetadata
LicenceTDM
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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 Socratic graph.