Quantile growth mixture modeling of weight loss for bariatric surgery patients

Music City Center 

The goal of Growth Mixture Modeling (GMM) is to identify underlying latent groups of units which are qualitatively different in their growth trajectories. Among the various assumptions needed for GMM to work, one that is often taken for granted is that residuals of the growth curve portion are assumed to be Normally distributed. Kim et al showed that violations of this assumption can have serious consequences for GMMs. Most notably, one may arrive at the incorrect number of latent classes due to "the relationship between class membership recovery and the proportion of outliers" in the sample of interest. As such, the use of traditional mean-based GMM could lead to misleading conclusions not just about the qualtitative differences between latent classes, but more fundamentally the numbers of latent classes themselves. As such, more robust approaches such as median-based (and by extension quantile-based) GMM are essential advancements to consider. In this paper we extend the median GMM to arbitrary quantiles of the weight loss distribution for a bariatric surgery cohort, by leveraging the location-scale mixture representation of the Asymmetric Laplace Distribution.

Growth mixture modeling

Quantile regression

Growth curve modeling 

Mental Health Statistics Section