Evidence map›Paper›PMID 42164993›Full record

ArticleJournal of statistical planning and inference2026

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data.

Daewoo Pak, Jianrui Zhang, Di Wu, Haolei Weng, Chenxi Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of statistical planning and inference, 2026. 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

5 authors.

Daewoo PakDivision of Data Science, Yonsei University, Wonju 26493, Korea.
Jianrui ZhangDepartment of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.
Di WuDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.
Haolei WengDepartment of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.
Chenxi LiDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.

Funding

UNIVERSITY OF PITTSBURGH CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTE: BPCAUL1RR024153 · NCRR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2006 to 2011
$73.9M
Psychosocial Influences on Rural Children's Oral HealthR01DE014899 · NIDCR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FOXMAN, BETSY, MARAZITA, MARY L. · 2002 to 2020
$39.9M
Fluoride & Other Factors in Childhood and Adolescent Bone DevelopmentR01DE012101 · NIDCR · UNIVERSITY OF IOWA · PI LEVY, STEVEN M. · 1998 to 2013
$11.9M
THERMAL ASSEMBLY OF MINERAL/COLLAGEN BIOMATERIALSP60DE013076 · NIDCR · UNIVERSITY OF IOWA · PI MESSERSMITH, PHILLIP B · 1999 to 2003
$8.4M
Genome Wide Association Coordinating CenterU01HG004446 · NHGRI · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2007 to 2011
$6.6M
LONGITUDINAL STUDY OF FLUORIDE EXPOSURES &FLUORIDER01DE009551 · NIDCR · UNIVERSITY OF IOWA · PI LEVY, STEVEN M. · 1991 to 2008
$4.7M
Dental Caries: Whole Genome Association and Gene x Environment StudiesU01DE018903 · NIDCR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MARAZITA, MARY L. · 2007 to 2010
$1.2M
Survival genetics methods for genetic association studies of early childhood cariesR56DE030437 · NIDCR · MICHIGAN STATE UNIVERSITY · PI LI, CHENXI · 2021 to 2021
$312k
Efficient methods for genome-wide survival analysis of early childhood cariesR03DE032357 · NIDCR · MICHIGAN STATE UNIVERSITY · PI LI, CHENXI · 2022 to 2023
$310k
NCRR NIH HHS UL1 RR024153NHGRI NIH HHS HHSN268200782096CNHGRI NIH HHS U01 HG004446NIDCR NIH HHS P60 DE013076NIDCR NIH HHS R01 DE009551NIDCR NIH HHS R01 DE012101NIDCR NIH HHS R01 DE014899NIDCR NIH HHS R03 DE032357NIDCR NIH HHS R56 DE030437NIDCR NIH HHS U01 DE018903
6 · The paper itself

Abstract

We develop a set of variable selection methods for the Cox model under interval censoring, in the ultra-high dimensional setting where the dimensionality can grow exponentially with the sample size. The methods select covariates via a penalized nonparametric maximum likelihood estimation with some popular penalty functions, including lasso, adaptive lasso, SCAD, and MCP. We prove that our penalized variable selection methods with folded concave penalties or adaptive lasso penalty enjoy the oracle property. Extensive numerical experiments show that the proposed methods have satisfactory empirical performance under various scenarios. The utility of the methods is illustrated through an application to a genome-wide association study of age to early childhood caries.

Indexed as

Cox modelinterval censoringoracle propertyultra-high dimensionvariable selection

Identifiers

PMID42164993
PMCPMC13186121

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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.