gms | German Medical Science

Kongress Medizin und Gesellschaft 2007

17. bis 21.09.2007, Augsburg

Graphical models illustrate complex associations and causal relations between variables describing human functioning

Meeting Abstract

  • Ralf Strobl - Institut für Gesundheits- und Rehabilitationswissenschaften, München
  • Eva Grill - Institut für Gesundheits- und Rehabilitationswissenschaften, München
  • Martin Müller - Institut für Gesundheits- und Rehabilitationswissenschaften, München
  • Sylvia Schick - Institut für Gesundheits- und Rehabilitationswissenschaften, München
  • Gerold Stucki - Institut für Gesundheits- und Rehabilitationswissenschaften, München
  • Ulrich Mansmann - Institut für medizinische Informationsverarbeitung, Biometrie und Epidemiologie, München

Kongress Medizin und Gesellschaft 2007. Augsburg, 17.-21.09.2007. Düsseldorf: German Medical Science GMS Publishing House; 2007. Doc07gmds410

The electronic version of this article is the complete one and can be found online at:

Published: September 6, 2007

© 2007 Strobl et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Introduction: Human functioning is serving as a main outcome in health studies. Due to the complex nature of human functioning, studies often concentrate on single aspects of human functioning such as mobility. While it is important to gain knowledge about the determinants of single aspects, a better understanding of the association structure of all components of human functioning might be beneficial for the design and planning of interventions. The analysis of multiple correlated variables, however, leads to the problem of multiple testing and variance inflation. To overcome this problem, graphical models have been used, albeit not for the analysis of functioning.

The aim of this study was to illustrate the application of graphical models and to gain insight into human functioning in patients after an acute disease or injury.

Material and Methods: We collected information on 113 second level categories of the International Classification of Functioning, Disability and Health (ICF) in a convenience sample of patients undergoing rehabilitation.

Meinshausen and Buehlmann [3] used the LASSO for linear models to obtain a neighbourhood estimator. For this task, we applied constrained logistic regression [2]. The outcome of this neighbourhood selection process was further enhanced by bootstrap aggregating [1] resulting in more stable graphs.

To overcome the problem of missing data, we generated multiple imputations for incomplete multivariate data by Gibbs Sampling.

Results: 616 patients (mean age 63, 54% female) were included in the analyses. The resulting subgraphs were stable and meaningful.

Discussion/Conclusion: In order to understand the complex structures of human functioning, the different aspects of functioning have to be decomposed. Modelling these many aspects may become a methodological challenge. Graphical modelling can be the basis for the identification of confounders, intermediate variables, meaningful dimensions, subsets for regression analysis, and intervention targets.


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