Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
- 1 June 1999
- journal article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 55 (2) , 463-469
- https://doi.org/10.1111/j.0006-341x.1999.00463.x
Abstract
Summary. 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.Keywords
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