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Cluster Analysis Applied to Symptom Ratings of Psychiatric Patients: An Evaluation of its Predictive Ability

Published online by Cambridge University Press:  29 January 2018

George W. Williams ,
Gail M. Barton ,
Andrew A. White  and
Hosik Won
George W. Williams
Affiliation:
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA 48109
Gail M. Barton
Affiliation:
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA 48109
Andrew A. White
Affiliation:
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA 48109
Hosik Won
Affiliation:
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA 48109
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Summary

Ratings on 39 symptoms were examined for patients admitted to the Neuropsychiatric Institute of the University of Michigan Medical Center. A detailed evaluation was made of the clusters derived by a hierarchical clustering algorithm, using complete linkage and a simple matching coefficient on the binary variables of presence or absence of symptoms. The four groups of patients suggested by the cluster analysis can be characterized as follows: (1) generalized multiplicity of symptoms; (2) capacity to cope except for orientation apart from generally held norms; (3) activity level and thought processes speeded up, intensified, and unselected; (4) inwardly punitive, slowed down and distressed. It is shown that these groups received significantly different treatment and that the effect of treatment was significantly different, while no such differences were noted for groups defined in terms of diagnoses. By means of linear discriminant functions, rules are suggested for assigning other psychiatric patients to one of these four groups.

Type
Research Article
Information
The British Journal of Psychiatry , Volume 129 , Issue 2 , August 1976 , pp. 178 - 185
Copyright
Copyright © Royal College of Psychiatrists, 1976 

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