Evidence map›Paper›PMID 41245172›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2025

Robust sparse Bayesian regression for longitudinal gene-environment interactions.

Kun Fan, Yu Jiang, Shuangge Ma, Weiqun Wang, Cen Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2025. 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.

Kun FanDepartment of Health Data Science and Biostatistics, Peter O'Donnell Jr School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.ORCID https://orcid.org/0009-0004-0084-8738
Yu JiangDivision of Epidemiology, Biostatistics and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, USA.
Shuangge MaDepartment of Biostatistics, Yale University, New Haven, CT, USA.ORCID https://orcid.org/0000-0001-9001-4999
Weiqun WangDepartment of Food Nutrition Dietetics and Health, Kansas State University, Manhattan, KS, USA.
Cen WuDepartment of Statistics, Kansas State University, Manhattan, KS, USA.ORCID https://orcid.org/0000-0002-5172-141X

Funding

Novel methods for identifying genetic interactions in cancer prognosisR01CA204120 · NCI · YALE UNIVERSITY · PI Shuangge Ma · 2016 to 2026
$3.5M
Mechanism of Cancer Prevention by Weight ControlR15CA167678 · NCI · KANSAS STATE UNIVERSITY · PI WANG, WEIQUN GEORGE · 2011 to 2011
$444k
NCI NIH HHS R01 CA204120NCI NIH HHS R15 CA167678
6 · The paper itself

Abstract

In longitudinal studies, repeated measure analysis of variance (ANOVA) is a classical analysis where selecting important main and interaction effects for accurate estimation and prediction is among one of its central goals. With high-dimensional genetic factors, ANOVA leads to a sparse longitudinal gene-environment (

Indexed as

longitudinal gene–environment interactionMCMC (Markov Chain Monte Carlo)quantile mixed-effects modelrobust Bayesian variable selectionstructured spike-and-slab priors

Identifiers

PMID41245172
PMCPMC12617437

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.