Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10912
Title: Latent class analysis of persistent disturbing behaviour patients by using longitudinal profiles
Authors: BRUCKERS, Liesbeth 
SERROYEN, Jan 
MOLENBERGHS, Geert 
Slaets, Herman
Goeyvaerts, Willem
Issue Date: 2010
Publisher: WILEY-BLACKWELL PUBLISHING, INC
Source: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 59. p. 495-512
Abstract: Persistent disturbing behaviour refers to a chronic condition in highly unstable, therapy resistant psychiatric patients. Because these patients are difficult to maintain in their natural living environment and even in hospital wards, purposely designed residential psychiatric facilities need to be established. Therefore, it is important to define and circumscribe the group carefully. Serroyen and co-workers, starting from the longitudinal analysis of a score based on data from the Belgian national psychiatric registry, undertook a discriminant analysis to distinguish persistent disturbing behaviour patients from a control group. They also indicated that there is scope for further subdividing the persistent disturbing behaviour patients into two subgroups, using conventional cluster analysis techniques. We employ a variety of novel longitudinal-data-based cluster analysis techniques. These are based on either conventional growth models, growth-mixture models or latent class growth models. Unlike in earlier analyses, where some evidence for two groups was found, there now is an indication of three groups, which is a finding with high practical and organizational relevance.
Notes: [Bruckers, Liesbeth] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. liesbeth.bruckers@uhasselt.be
Keywords: growth curves; growth mixture models; latent class growth models; linear mixed models;Growth curves; Growth mixture models; Latent class growth models; Linear mixed models
Document URI: http://hdl.handle.net/1942/10912
ISSN: 0035-9254
e-ISSN: 1467-9876
DOI: 10.1111/j.1467-9876.2009.00704.x
ISI #: 000276500600007
Category: A1
Type: Journal Contribution
Validations: ecoom 2011
Appears in Collections:Research publications

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