Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
Article first published online: 26 MAY 2004
Volume 55, Issue 2, pages 463–469, June 1999
How to Cite
Muthén, B. and Shedden, K. (1999), Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55: 463–469. doi: 10.1111/j.0006-341X.1999.00463.x
- Issue published online: 26 MAY 2004
- Article first published online: 26 MAY 2004
- Received October 1997. Revised May 1998. Accepted July 1998.
- Growth modeling;
- Latent class analysis;
- Latent variables;
- Maximum likelihood;
- Trajectory classes
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.