Evidence map›Paper›PMID 34637449›Full record

ArticlePloS one2021

Mediation model with a categorical exposure and a censored mediator with application to a genetic study.

Jian Wang, Jing Ning, Sanjay Shete

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Jian WangDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Jing NingDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Sanjay SheteDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.ORCID 0000-0001-7622-376X

Funding

TRANSLATIONAL AND ANALYTICAL CHEMISTRY COREP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI PETER W PISTERS · 1985 to 2026
$279.3M
CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR024156 · NCRR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2006 to 2011
$53.0M
SUBCLINICAL CARDIOVASCULAR DISEASE STUDYN01HC95166 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI TRACY, RUSSELL P · 2007 to 2014
$4.0M
Multi-level Evaluation of Racial/ethnic Disparities in Liver Disease OutcomesR01CA256977 · NCI · BAYLOR COLLEGE OF MEDICINE · PI KANWAL, FASIHA, SINGAL, AMIT · 2021 to 2025
$3.0M
Novel analysis of association between microbiome and treatment infection in AMLR01AI143886 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HU, JIANHUA · 2019 to 2023
$2.0M
Cigarette Smoke and Susceptibility to Influenza InfectionR01HL095163 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JASPERS, ILONA, NOAH, TERRY L · 2009 to 2013
$2.0M
Adjunctive Use of Apyrase to Fibrinolytic TherapyR44HL095169 · NHLBI · APT THERAPEUTICS, INC. · PI CHEN, RIDONG · 2012 to 2013
$1.3M
Comparative Effectiveness of Cancer Research: Use Data from Multiple SourcesR01CA193878 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI NING, JING · 2016 to 2019
$1.2M
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTERN01HC095162 · HC · JOHNS HOPKINS UNIVERSITY · PI SZKLO, MOYSES A · 1999 to 2000
$694k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC095161 · HC · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SHEA, STEVEN · 1999 to 2001
$642k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC095165 · HC · WAKE FOREST UNIVERSITY · PI BURKE, GREGORY L · 1999 to 2001
$616k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC095160 · HC · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SAAD, MOHAMMED F · 1999 to 2001
$592k
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA193878NCI NIH HHS R01 CA256977NCRR NIH HHS UL1 RR024156NHLBI NIH HHS N01 HC065226NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS N01HC95166NIAID NIH HHS R01 AI143886
6 · The paper itself

Abstract

Mediation analysis is a statistical method for evaluating the direct and indirect effects of an exposure on an outcome in the presence of a mediator. Mediation models have been widely used to determine direct and indirect contributions of genetic variants in clinical phenotypes. In genetic studies, the additive genetic model is the most commonly used model because it can detect effects from either recessive or dominant models (or any model in between). However, the existing approaches for mediation model cannot be directly applied when the genetic model is additive (e.g. the most commonly used model for SNPs) or categorical (e.g. polymorphic loci), and thus modification to measures of indirect and direct effects is warranted. In this study, we proposed overall measures of indirect, direct, and total effects for a mediation model with a categorical exposure and a censored mediator, which accounts for the frequency of different values of the categorical exposure. The proposed approach provides the overall contribution of the categorical exposure to the outcome variable. We assessed the empirical performance of the proposed overall measures via simulation studies and applied the measures to evaluate the mediating effect of a women's age at menopause on the association between genetic variants and type 2 diabetes.

Indexed as

Models, GeneticAge FactorsComputer SimulationDiabetes Mellitus, Type 2FemaleHumansMenopauseMiddle AgedModels, StatisticalPolymorphism, Single Nucleotide

Identifiers

PMID34637449
PMCPMC8509986

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.