Estimating transdiagnostic symptom networks: The problem of "skip outs" in diagnostic interviews.
Journal
Psychological assessment
ISSN: 1939-134X
Titre abrégé: Psychol Assess
Pays: United States
ID NLM: 8915253
Informations de publication
Date de publication:
Jan 2019
Jan 2019
Historique:
pubmed:
31
8
2018
medline:
27
2
2019
entrez:
31
8
2018
Statut:
ppublish
Résumé
Network models of the symptoms of psychological disorders provide a novel lens for examining comorbidity. Viewing symptoms as causal entities in their own right, researchers can attempt to identify specific symptoms that "bridge" diagnostic entities, providing a more granular perspective on comorbidity than the one provided by analysis at the syndromal level. Such analyses could help identify transdiagnostic targets for both research and clinical interventions. Although promising conceptually, extant work using this approach often uses structured diagnostic interview data that employ "skip outs," branching logic conditional on responses to gating questions (which may be criterial or risk markers). We demonstrate that skip outs, where screening items are asked for each disorder before assessing the remaining symptoms, can produce significant problems in interpretation of comorbidity between symptoms and, hence, transdiagnostic network models. The nature and extent of this problem is explored, and suggestions for future network studies of comorbidity are provided. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Identifiants
pubmed: 30160496
pii: 2018-42554-001
doi: 10.1037/pas0000644
pmc: PMC6312735
mid: NIHMS985542
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
73-81Subventions
Organisme : NIAAA NIH HHS
ID : K05 AA017242
Pays : United States
Organisme : National Institutes of Health
Organisme : NIAAA NIH HHS
ID : P50 AA010761
Pays : United States
Organisme : NIAAA NIH HHS
ID : T32 AA013526
Pays : United States
Organisme : NIAAA NIH HHS
ID : R01 AA024133
Pays : United States
Organisme : NIAAA NIH HHS
ID : R01 AA023248
Pays : United States
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