title: Finite mixture modeling with mixture outcomes using the EM algorithm.
abstract: This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random-coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.
authors.affiliation: Graduate School of Education and Information Studies and Department of Statistics, University of California, Los Angeles, California 90095-1521, USA. bmuthen@ucla.edu
authors.forename: B
authors.initials: B
authors.lastname: Muthén
authors.forename: K
authors.initials: K
authors.lastname: Shedden
mesh_terms: D000293:Adolescent; D000328:Adult; D000428:Alcohol Drinking / Q000009:adverse effects; D000437:Alcoholism / Q000209:etiology; D000465:Algorithms; D001699:Biometry; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical