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Activity Number: 34
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #313328 View Presentation
Title: Investigating Differential Changes Using Mixture Latent Change Score (MLCS) Models
Author(s): Emil Coman*+ and Judith Fifield and John J. McArdle and Monique Davis-Smith
Companies: Ethel Donaghue TRIPP Center and Ethel Donaghue TRIPP Center and University of Southern California and Mercer University School of Medicine
Keywords: latent class models ; latent change scores ; mixture modeling ; comparative effectiveness
Abstract:

We introduce the Mixture Latent Change Score (MLCS) longitudinal models for investigating differential changes in unobserved (latent) groups of patients. We present a description of Latent Change Score (LCS) structural models, show their utility in replicating known models of change, like repeated measures ANOVA and Latent Growth Models (LGM), then illustrate several modeling extensions, and the advantages of MLCS over the Growth Mixture Models (GMM), when inferring differential complex trajectories of changes in unobserved classes. We exemplify with a four wave data from an intervention to prevent diabetes in prediabetics (CDC's Diabetes Prevention Program). The MLCS model is able to extract more meaningful latent classes than GMM, and in our example it extracted classes that appeared to be more balanced, and had nuanced trajectories that are clinically meaningful, like an initial abrupt improvement, followed by a plateau, then slight decline, and another with initial setback, plateau, then some improvement. We present limitations and challenges in specifying dynamic mixture models of change, and offer suggestions for extensions, like dynamic mixture mediation models.


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