Evidence map›Paper›PMID 35429920›Full record

ArticleAddictive behaviors2022

Cross-sectional and longitudinal AUD symptom networks: They tell different stories.

William E Conlin, Michaela Hoffman, Douglas Steinley, Kenneth J Sher

Abstract read
In one paragraph

Article in Addictive behaviors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Network analysis of DSM-5 criteria for gambling disorder: considering sex differences in a large clinical sample.European psychiatry : the journal of the Association of European Psychiatrists · 2024
    Article
  9. Article
  10. Predictors of symptom course in alcohol use disorder.Alcohol, clinical & experimental research · 2023
    Article
  11. Article
  12. Article
  13. 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

4 authors.

William E ConlinDepartment of Psychological Sciences, University of Missouri, United States. Electronic address: wecxk6@umsystem.edu.
Michaela HoffmanDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, United States.
Douglas SteinleyDepartment of Psychological Sciences, University of Missouri, United States.
Kenneth J SherDepartment of Psychological Sciences, University of Missouri, United States.

Funding

TREATING ETHANOL WITHDRAWAL WITH LORAZEPAM/NALTREXONEP50AA010761 · NIAAA · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI HOWARD C. BECKER · 1996 to 2026
$46.8M
Psychology of Alcohol Use and Dependence TrainingT32AA013526 · NIAAA · UNIVERSITY OF MISSOURI-COLUMBIA · PI DENIS M MCCARTHY, KENNETH JAMES SHER · 2002 to 2026
$9.0M
Refining the Diagnosis of Alcohol Use Disorder: A Comprehensive ApproachR01AA024133 · NIAAA · UNIVERSITY OF MISSOURI-COLUMBIA · PI SHER, KENNETH JAMES, STEINLEY, DOUGLAS L · 2015 to 2018
$2.1M
Using Symptom Network Models to Translate Theory to Clinical ApplicationsF31AA029949 · NIAAA · UNIVERSITY OF MISSOURI-COLUMBIA · PI CONLIN, WILLIAM E · 2021 to 2023
$75k
NIAAA NIH HHS F31 AA029949NIAAA NIH HHS P50 AA010761NIAAA NIH HHS R01 AA024133NIAAA NIH HHS T32 AA013526
6 · The paper itself

Abstract

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.

Indexed as

AlcoholismAlcohol-Related DisordersAlcohol DrinkingCross-Sectional StudiesHumansAddictionAlcohol use disorderCross-lagged panel network analysisSymptom networksTemporal networks

Identifiers

PMID35429920
PMCPMC9491298

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.