title: On Inclusion of Covariates for Class Enumeration of Growth Mixture Models.
abstract: In this article, we directly questioned the common practice in growth mixture model (GMM) applications that exclusively rely on the fitting model without covariates for GMM class enumeration. We provided theoretical and simulation evidence to demonstrate that exclusion of covariates from GMM class enumeration could be problematic in many cases. Based on our findings, we provided recommendations for examining the class enumeration by the fitting model without covariates and discussed the potential of covariate inclusion as a remedy for the weakness of GMM class enumeration without including covariates. A real example on the development of children's cumulative exposure to risk factors for adolescent substance use was provided to illustrate our methodological developments.
authors.affiliation: UCLA Integrated Substance Abuse Programs, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90025, USA.
authors.forename: Libo
authors.initials: L
authors.lastname: Li
authors.forename: Yih-Ing
authors.initials: YI
authors.lastname: Hser
keywords: Growth mixture model; class enumeration; covariate; misspecification
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