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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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References

Ahmed, AO, Buckley, PF, Mabe, PA (2012 a). Latent structure of psychotic experiences in the general population. Acta Psychiatrica Scandinavica 125, 54–65.Google Scholar
Ahmed, AO, Green, BA, Buckley, PF, McFarland, ME (2012 b). Taxometric analyses of paranoid and schizoid personality disorders. Psychiatry Research 196, 123–132.Google Scholar
Ahmed, AO, Green, BA, Goodrum, NM, Doane, NJ, Birgenheir, D, Buckley, PF (2013). Does a latent class underlie schizotypal personality disorder? Implications for schizophrenia. Journal of Abnormal Psychology 122, 475–491.Google Scholar
Ahmed, AO, Strauss, GP, Buchanan, RW, Kirkpatrick, B, Carpenter, WT (2015). Are negative symptoms dimensional or categorical? Detection and validation of deficit schizophrenia with taxometric and latent variable mixture models. Schizophrenia Bulletin 41, 879–891.CrossRefGoogle ScholarPubMed
Akaike, H (1987). Factor analysis and AIC. Psychometrika 52, 317–332.Google Scholar
Allardyce, J, Suppes, T, van Os, J (2007). Dimensions and the psychosis phenotype. International Journal of Methods in Psychiatric Research 16 (Suppl. 1), S34–S40.Google Scholar
Arminger, G, Stein, P, Wittenberg, J (1999). Mixtures of conditional mean and covariance structure models. Psychometrika 64, 475–494.Google Scholar
Bakhshaie, J, Sharifi, V, Amini, J (2011). Exploratory factor analysis of SCL90-R symptoms relevant to psychosis. Iranian Journal of Psychiatry 6, 128–132.Google Scholar
Bauer, DJ, Curran, PJ (2003). Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychological Methods 8, 338–363.Google Scholar
Clark, SL, Muthén, B, Kaprio, J, D'Onofrio, BM, Viken, R, Rose, RJ, Smalley, SL (2013). Models and strategies for factor mixture analysis: two examples concerning the structure underlying psychological disorders. Structural Equation Modeling: A Multidisciplinary Journal 20, 681–703.Google Scholar
Collins, LM, Lanza, ST (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. John Wiley & Sons: New York.Google Scholar
Craddock, N, Owen, MJ (2005). The beginning of the end for the Kraepelinian dichotomy. British Journal of Psychiatry 186, 364–366.Google Scholar
Crowley, SL, Fan, X (1997). Structural equation modeling: basic concepts and applications in personality assessment research. Journal of Personality Assessment 68, 508–531.Google Scholar
Curran, PJ, Hussong, AM (2003). The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology 112, 526–544.Google Scholar
Dolan, CV, van der Maas, HLJ (1998). Fitting multivariate normal finite mixtures subject to structural equation modeling. Psychometrika 63, 225–253.CrossRefGoogle Scholar
Eaton, NR, Krueger, RF, Docherty, AR, Sponheim, SR (2014). Toward a model-based approach to the clinical assessment of personality psychopathology. Journal of Personality Assessment 96, 283–292.Google Scholar
Fonseca-Pedrero, E, Lemos-Giráldez, S, Paino, M, Sierra-Baigrie, S, Villazón-García, Ú, García-Portilla González, MP, Muñiz, J (2010). Dimensionality of hallucinatory predisposition: confirmatory factor analysis of the Launay–Slade Hallucination Scale-revised in college students. Anales de Psicología 26, 41–48.Google Scholar
Gale, CK, Wells, JE, McGee, MA, Oakley Browne, MA (2011). A latent class analysis of psychosis-like experiences in the New Zealand Mental Health Survey. Acta Psychiatrica Scandinavica 124, 205–213.CrossRefGoogle ScholarPubMed
Geiser, C (2013). Data Analysis with Mplus. The Guilford Press: New York.Google Scholar
Hagenaars, JA, McCutcheon, AL (2002). Applied Latent Class Analysis, pp. 476. Kluwer: Dordrecht, The Netherlands.Google Scholar
Hallquist, MN, Wright, AGC (2014). Mixture modeling methods for the assessment of normal and abnormal personality part I: cross-sectional models. Journal of Personality Assessment 96, 256–268.Google Scholar
Haslam, N, Holland, E, Kuppens, P (2012). Categories versus dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychological Medicine 42, 903–920.CrossRefGoogle ScholarPubMed
Heering, HD, van Haren, NEM, Derks, EM (2013). A two-factor structure of first rank symptoms in patients with a psychotic disorder. Schizophrenia Research 147, 269–274.Google Scholar
Hoyle, RH (editor) (2012). Handbook of Structural Equation Modeling. The Guilford Press: New York.Google Scholar
Kendler, KS, Gallagher, TJ, Abelson, JM, Kessler, RC (1996). Lifetime prevalence, demographic risk factors, and diagnostic validity of nonaffective psychosis as assessed in a US community sample: the National Comorbidity Survey. Archives of General Psychiatry 53, 1022–1031.CrossRefGoogle Scholar
Kessler, RC, McGonagle, KA, Zhao, S, Nelson, CB, Hughes, M, Eshleman, S, Wittchen, H-U, Kendler, KS (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Archives of General Psychiatry 51, 8–19.CrossRefGoogle ScholarPubMed
Kitamura, T, Okazaki, Y, Fujinawa, A, Takayanagi, I, Kasahara, Y (1998). Dimensions of schizophrenic positive symptoms: an exploratory factor analysis investigation. European Archives of Psychiatry and Clinical Neuroscience 248, 130–135.Google Scholar
Kuo, PH, Aggen, SH, Prescott, CA, Kendler, KS, Neale, MC (2008). Using a factor mixture modeling approach in alcohol dependence in a general population sample. Drug and Alcohol Dependence 98, 105–114.Google Scholar
Lubke, GH, Miller, PJ (2015). Does nature have joints worth carving? A discussion of taxometrics, model-based clustering and latent variable mixture modeling. Psychological Medicine 45, 705–715.CrossRefGoogle ScholarPubMed
Lubke, GH, Muthén, B (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods 10, 21–39.Google Scholar
Lubke, G, Muthén, B (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling 14, 26–47.Google Scholar
MacCallum, RC, Austin, JT (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology 51, 201–226.Google Scholar
Mamah, D, Owoso, A, Mbwayo, AW, Mutiso, VN, Muriungi, SK, Khasakhala, LI, Barch, DM, Ndetei, DM (2013). Classes of psychotic experiences in Kenyan children and adolescents. Child Psychiatry and Human Development 44, 452–459.Google Scholar
Marsh, HW, Lüdtke, O, Trautwein, U, Morin, AJS (2009). Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling: A Multidisciplinary Journal 16, 191–225.Google Scholar
Masyn, KE, Henderson, CE, Greenbaum, PE (2010). Exploring the latent structures of psychological constructs in social development using the dimensional–categorical spectrum. Social Development 19, 470–493.Google Scholar
Meehl, PE (1999). Clarifications about taxometric method. Applied and Preventive Psychology 8, 165–174.Google Scholar
Murphy, J, Shevlin, M, Adamson, G, Houston, JE (2010). Positive psychosis symptom structure in the general population: assessing dimensional consistency and continuity from “pathology” to “normality”. Psychosis: Psychological, Social and Integrative Approaches 2, 199–209.Google Scholar
Muthén, B (2001). Latent variable mixture modeling. In New Developments and Techniques in Structural Equation Modeling. (ed. Marcoulides, G. A. and Schumacker, R. E.), pp. 1–33. Lawrence Erlbaum Associates: Mahwah, NJ.Google Scholar
Muthén, B (2006). Should substance use disorders be considered as categorical or dimensional? Addiction 101 (Suppl. 1), 6–16.Google Scholar
Muthén, B, Asparouhov, T (2006). Item response mixture modeling: application to tobacco dependence. Addictive Behaviors 31, 1050–1066.Google Scholar
Muthén, LK, Muthén, BO (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling 9, 599–620.Google Scholar
Muthén, LK, Muthén, BO (2008). Mplus (version 5.1) . Muthén & Muthén: Los Angeles, CA.Google Scholar
Ndetei, DM, Muriungi, SK, Owoso, A, Mutiso, VN, Mbwayo, AW, Khasakhala, LI, Barch, DM, Mamah, D (2012). Prevalence and characteristics of psychotic-like experiences in Kenyan youth. Psychiatry Research 196, 235–242.Google Scholar
Nelson, TD, Aylward, BS, Steele, RG (2008). Structural equation modeling in pediatric psychology: overview and review of applications. Journal of Pediatric Psychology 33, 679–687.Google Scholar
Nylund, KL, Asparouhov, T, Muthén, B (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal 14, 535–569.Google Scholar
Ram, N, Grimm, KJ (2009). Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development 33, 565–576.Google Scholar
Raykov, T, Tomer, A, Nesselroade, JR (1991). Reporting structural equation modeling results in psychology and aging: some proposed guidelines. Psychology and Aging 6, 499–503.Google Scholar
Reynolds, CA, Raine, A, Mellingen, K, Venables, PH, Mednick, SA (2000). Three-factor model of schizotypal personality: invariance across culture, gender, religious affiliation, family adversity, and psychopathology. Schizophrenia Bulletin 26, 603–618.Google Scholar
Schwarz, G (1978). Estimating the dimension of a model. Annals of Statistics 6, 461–464.CrossRefGoogle Scholar
Sclove, LS (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika 52, 333–343.Google Scholar
Shevlin, M, Murphy, J, Dorahy, MJ, Adamson, G (2007). The distribution of positive psychosis-like symptoms in the population: a latent class analysis of the National Comorbidity Survey. Schizophrenia Research 89, 101–109.Google Scholar
Sterba, SK, Bauer, DJ (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology 22, 239–254.Google Scholar
Strauss, JS (1969). Hallucinations and delusions as points on continua function. Rating scale evidence. Archives of General Psychiatry 21, 581–586.Google Scholar
Streiner, DL (2005). Finding our way: an introduction to path analysis. Canadian Journal of Psychiatry 50, 115–122.Google Scholar
Streiner, DL (2006). Building a better model: an introduction to structural equation modeling. Canadian Journal of Psychiatry 51, 317–324.Google Scholar
Tandon, R, Keshavan, MS, Nasrallah, HA (2008). Schizophrenia, “Just the Facts”: what we know in 2008. Schizophrenia Research 100, 4–19.Google Scholar
Vermunt, JK, Magidson, J (2000). Latent class models for classification. Computational Statistics 41, 531–537.Google Scholar
Vermunt, JK, Magidson, J (2002). Latent class cluster analysis. In Applied Latent Class Analysis (ed. Hagenaars, J. A. and McCutcheon, A. L.), pp. 89–106. Cambridge University Press: Cambridge, UK.Google Scholar
Wolf, EJ, Harrington, KM, Clark, SL, Miller, MW (2013). Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety. Educational and Psychological Measurement 73, 913–934.Google Scholar
Wright, AGC, Hallquist, MN (2014). Mixture modeling methods for the assessment of normal and abnormal personality part II: longitudinal models. Journal of Personality Assessment 96, 269–282.Google Scholar
Wright, AGC, Krueger, RF, Hobbs, MJ, Markon, KE, Eaton, NR, Slade, T (2013). The structure of psychopathology: toward an expanded quantitative empirical model. Journal of Abnormal Psychology 122, 281–294.Google Scholar
Wuthrich, VM, Bates, TC (2006). Confirmatory factor analysis of the three-factor structure of the Schizotypal Personality Questionnaire and Chapman schizotypy scales. Journal of Personality Assessment 87, 292–304.Google Scholar
Yung, YF (1997). Finite mixtures in confirmatory factor analysis models. Psychometrika 62, 297–330.Google Scholar
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