Open AccessAssociation chain graphs: modelling etiological pathways
Author(s) -
Höfler Michael,
Wittchen HansUlrich,
Lieb Roselind,
Hoyer Jürgen,
Friis Robert H.
Publication year - 2003
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.144
Subject(s) - conditional independence , directed acyclic graph , graphical model , association (psychology) , independence (probability theory) , mathematics , computer science , combinatorics , statistics , psychology , psychotherapist
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, more specific information can be displayed whenever the data are essentially described only with statistical main‐ and two‐way interaction effects. Copyright © 2003 Whurr Publishers Ltd.