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Latent variable mixture modeling in psychiatric research – a review and application

Published online by Cambridge University Press:  03 November 2015

J. Miettunen ,
T. Nordström ,
M. Kaakinen  and
A. O. Ahmed
J. Miettunen*
Affiliation:
Center for Life Course Epidemiology and Systems Medicine, University of Oulu, Oulu, Finland Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland Department of Psychiatry, Oulu University Hospital, Oulu, Finland
T. Nordström
Affiliation:
Center for Life Course Epidemiology and Systems Medicine, University of Oulu, Oulu, Finland Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
M. Kaakinen
Affiliation:
Center for Life Course Epidemiology and Systems Medicine, University of Oulu, Oulu, Finland Unit of General Practice, Oulu University Hospital, Oulu, Finland Biocenter Oulu, University of Oulu, Oulu, Finland Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
A. O. Ahmed
Affiliation:
Department of Psychiatry, Weill Cornell Medical College – Westchester Division, White Plains, NY, USA
*
*Address for correspondence: J. Miettunen, Center for Life Course Epidemiology and Systems Medicine, PO Box 5000, 90014 University of Oulu, Finland. (Email: [email protected])
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Abstract

Latent variable mixture modeling represents a flexible approach to investigating population heterogeneity by sorting cases into latent but non-arbitrary subgroups that are more homogeneous. The purpose of this selective review is to provide a non-technical introduction to mixture modeling in a cross-sectional context. Latent class analysis is used to classify individuals into homogeneous subgroups (latent classes). Factor mixture modeling represents a newer approach that represents a fusion of latent class analysis and factor analysis. Factor mixture models are adaptable to representing categorical and dimensional states of affairs. This article provides an overview of latent variable mixture models and illustrates the application of these methods by applying them to the study of the latent structure of psychotic experiences. The flexibility of latent variable mixture models makes them adaptable to the study of heterogeneity in complex psychiatric and psychological phenomena. They also allow researchers to address research questions that directly compare the viability of dimensional, categorical and hybrid conceptions of constructs.

Keywords

Factor mixture modelslatent class analysispsychosisstatistics
Type
Review Article
Information
Psychological Medicine , Volume 46 , Issue 3 , February 2016 , pp. 457 - 467
Copyright
Copyright © Cambridge University Press 2015 

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