Mixture Modeling: A Useful Analytical Approach for Drug Use Studies

The analytic methods often used in drug use studies, such as ANOVA, multiple regression, logistic regression, multilevel models, and structural equation modeling (SEM) including path analysis, factor analysis, and latent growth curve model, are variable-centered approaches. Those approaches assume that the study sample arises from a homogeneous population; and focus on relations among variables, such as effects of independent variables on dependent variables. When typology of drug use or pattern of growth trajectories of drug use practices is of interest, person-centered analytical approach should be applied. In recent years, mixture modeling a person-centered analytic approach has increasingly gained its popularity in many research fields, including drug use studies. Assuming a heterogeneous population, mixture modeling aims to identify a finite number of subpopulations, called latent classes, that are unknown a priori within the population under study [1,2-4]. Individuals within a latent class share characteristics thus are more similar than those between latent classes. Mixture modeling can be readily applied to both cross-sectional and longitudinal studies.