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Seeing the invisible in complex data : how graphical chains can model pathways into disease

Höfler, M. and Lieb, R. and Hoyer, J. and Friis, R. H. and Wittchen, H.-U.. (2003) Seeing the invisible in complex data : how graphical chains can model pathways into disease. International journal of methods in psychiatric research, Vol. 12, no. 2. pp. 77-84.

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Official URL: http://edoc.unibas.ch/dok/A5253426

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Abstract

Multiple time-dynamic and interrelated risk factors are usually involved in the complex etiology of disorders. This paper presents a strategy to explore and display visually the relative importance of different association pathways for the onset of disorder over time. The approach is based on graphical chain models, a tool that is Powerful but still under-utilized in most fields. Usually, the results of these models are displayed using directed acyclic graphs (DAGs). These draw an edge between a pair of variables whenever the assumption of conditional independence given variables on an earlier or equal temporal footing is violated to a statistically significant extent. In the present paper, the graphs are modified in that confidence intervals for the strengths of associations (statistical main effects) are visualized. These new graphs are called association chain graphs (ACGS). Statistical interactions cause 'edges' between the respective variables within the DAG framework (because the assumption of conditional independence is violated). In contrast they are represented as separate graphs within the subsample where the different association chains may work within the ACG framework. With this new type of graph, mare specific information can he displayed whenever the data are essentially described only with statistical main- and two-way interaction effects.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Klinische Psychologie und Neurowissenschaften > Klinische Psychologie und Epidemiologie (Lieb)
UniBasel Contributors:Lieb, Roselind
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Wiley
ISSN:1049-8931
Note:Publication type according to Uni Basel Research Database: Journal article
Last Modified:22 Mar 2012 14:25
Deposited On:22 Mar 2012 13:43

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