Evidence map›Paper›PMID 22082343›Full record

ArticlePsychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors2012

Are the symptoms of cannabis use disorder best accounted for by dimensional, categorical, or factor mixture models? A comparison of male and female young adults.

Nathan A Gillespie, Michael C Neale, Lisa N Legrand, William G Iacono, Matt McGue

Abstract read
In one paragraph

Article in Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Characterizing severe and enduring anorexia nervosa: An empirical approach.The International journal of eating disorders · 2017
    Article
  7. Article
  8. Article
  9. Article
  10. 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.

Nathan A GillespieVirginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23219-1534, USA. ngillespie@vcu.edu
Michael C Neale
Lisa N Legrand
William G Iacono
Matt McGue

Funding

Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentU01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2007 to 2010
$10.8M
TWIN STUDY OF FEMALE ALCOHOLISMS AND OTHER DISORDERSR01AA009367 · NIAAA · UNIVERSITY OF MINNESOTA TWIN CITIES · PI MCGUE, MATTHEW K. · 1992 to 2013
$9.0M
Twin Family Study of Vulnerability to Substance AbuseR37DA005147 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2009 to 2018
$6.4M
TWIN/FAMILY STUDY OF VULNERABILITY TO SUBSTANCE ABUSER01DA005147 · NIDA · UNIVERSITY OF MINNESOTA TWIN CITIES · PI IACONO, WILLIAM G · 1987 to 2007
$5.5M
Adolescent drinking and midlife outcomes: A prospective cotwin control studyR37AA009367 · NIAAA · UNIVERSITY OF MINNESOTA · PI MCGUE, MATTHEW K. · 2016 to 2024
$5.4M
Psychometric and Genetic Assessments of Substance UseR37DA018673 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI NEALE, MICHAEL CHURTON · 2005 to 2014
$3.7M
Research Education in Statistical Genetics of Substance AbuseR25DA026119 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI NEALE, MICHAEL CHURTON · 2008 to 2018
$3.2M
Psychometric and Genetic Assessments of Substance UseR01DA018673 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI NEALE, MICHAEL CHURTON · 2004 to 2019
$2.3M
Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentR01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2011 to 2011
$1.6M
Genetic & environmental pathways to drug use, abuse & dependenceR00DA023549 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI GILLESPIE, NATHAN ALEXANDER · 2010 to 2012
$717k
Genetic & environmental pathways to drug use, abuse & dependenceK99DA023549 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI GILLESPIE, NATHAN ALEXANDER · 2008 to 2009
$166k
NIAAA NIH HHS AA09367NIAAA NIH HHS R01 AA009367NIAAA NIH HHS R37 AA009367NIDA NIH HHS 1K99DA023549-01A2NIDA NIH HHS DA024417NIDA NIH HHS DA05147NIDA NIH HHS DA-18673NIDA NIH HHS K99 DA023549NIDA NIH HHS R00 DA023549NIDA NIH HHS R00DA023549NIDA NIH HHS R01 DA005147NIDA NIH HHS R01 DA018673NIDA NIH HHS R01 DA024417NIDA NIH HHS R25 DA026119NIDA NIH HHS R37 DA005147NIDA NIH HHS R37 DA018673NIDA NIH HHS U01 DA024417
6 · The paper itself

Abstract

Despite the consensus that criteria for cannabis abuse and dependence and symptoms of withdrawal are best explained by a single latent liability, it remains unknown whether alternative models provide a better explanation of these criteria. A series of latent factor, latent class, and hybrid factor mixture models were fitted to data from 872 recent cannabis users from the Minnesota Twin Family Study who completed Diagnostic and Statistical Manual of Mental Disorders (3rd ed., revised, and 4th ed.) diagnostic criteria for cannabis abuse, dependence, and symptoms of withdrawal. Despite theoretical appeal, results did not support latent class or factor mixture modeling. Instead, symptoms of abuse, dependence, and withdrawal were better summarized by a single latent factor Cannabis Use Disorder (CUD) for male and female young adults. An almost 2-fold sex difference in item endorsement was best explained by a single mean difference on the CUD factor, indicating that young men have a greater latent liability toward expressing CUD.

Indexed as

Models, TheoreticalAdolescentAdultDiagnostic and Statistical Manual of Mental DisordersFemaleFollow-Up StudiesHumansLongitudinal StudiesMaleMarijuana AbuseSex Factors

Identifiers

PMID22082343
PMCPMC3302972

What OpenQuestion holds

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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 OpenQuestion graph.