Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

ABSTRACT: Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the

SUBMITTER: Lamont AE 

PROVIDER: S-EPMC4865372 | biostudies-literature | 2016 Jan-Feb

REPOSITORIES: biostudies-literature