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Using directed acyclic graphs in epidemiological research in psychosis : an analysis of the role of bullying in psychosis

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Moffa, Giusi, Catone, Gennaro, Kuipers, Jack, Kuipers, Elizabeth, Freeman, Daniel, Marwaha, Steven, Lennox, Belinda R., Broome, Matthew R. and Bebbington, Paul (2017) Using directed acyclic graphs in epidemiological research in psychosis : an analysis of the role of bullying in psychosis. Schizophrenia Bulletin, 43 (6). pp. 1273-1279. doi:10.1093/schbul/sbx013 ISSN 0586-7614.

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Official URL: http://dx.doi.org/10.1093/schbul/sbx013

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Abstract

Modern psychiatric epidemiology researches complex interactions between multiple variables in large datasets. This creates difficulties for causal inference. We argue for the use of probabilistic models represented by directed acyclic graphs (DAGs). These capture the dependence structure of multiple variables and, used appropriately, allow more robust conclusions about the direction of causation. We analyzed British national survey data to assess putative mediators of the association between bullying victimization and persecutory ideation. We compared results using DAGs and the Karlson–Holm–Breen (KHB) logistic regression commands in STATA. We analyzed data from the 2007 English National Survey of Psychiatric Morbidity, using the equivalent 2000 survey in an instant replication. Additional details of methods and results are provided in the supplementary material. DAG analysis revealed a richer structure of relationships than could be inferred using the KHB logistic regression commands. Thus, bullying had direct effects on worry, persecutory ideation, mood instability, and drug use. Depression, sleep and anxiety lay downstream, and therefore did not mediate the link between bullying and persecutory ideation. Mediation by worry and mood instability could not be definitively ascertained. Bullying led to hallucinations indirectly, via persecutory ideation and depression. DAG analysis of the 2000 dataset suggested the technique generates stable results. While causality cannot be fully determined from cross-sectional data, DAGs indicate the relationships providing the best fit. They thereby advance investigation of the complex interactions seen in psychiatry, including the mechanisms underpinning psychiatric symptoms. It may consequently be used to optimize the choice of intervention targets.

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Mental Health and Wellbeing
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH): Psychoses -- Diagnosis -- Statistical methods
Journal or Publication Title: Schizophrenia Bulletin
Publisher: Oxford University Press
ISSN: 0586-7614
Official Date: 21 October 2017
Dates:
DateEvent
21 October 2017Published
17 May 2017Available
19 January 2017Accepted
Volume: 43
Number: 6
Page Range: pp. 1273-1279
DOI: 10.1093/schbul/sbx013
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 19 May 2017
Date of first compliant Open Access: 19 May 2017
Funder: National Institute for Health Research (Great Britain) (NIHR), Maudsley NHS Foundation Trust

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