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Joint Modeling of Mean and Variance in Longitudinal Data: Shared Random Effects vs. Latent Class Models
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Bei Jiang, Columbia University 
*Michael Elliott, University of Michigan 
Mary Sammel, University of Pennsylvania 
Naisyn Wang, University of Michigan 

Keywords: shared random effect, latent class, women's health

Joint modeling methods have become popular tools to link longitudinal data to a primary event. These models typically use either a shared random effect or latent class model to link information extracted from the longitudinal data to prediction of the primary event. This work shows results of a simulation study to compare and contrast these two modeling strategies; in particular, we study in detail the effects of the primary outcome model misspecification. Among other findings, we note that when we analyze data from a shared random-effect using a latent class model while the information from the longitudinal data is weak, the latent class approach is more sensitive to such a model misspecification. Under this setting, the latent class model has a superior performance in within-sample prediction that cannot be duplicated when predicting new samples. This is a unique feature of the latent class approach that is new as far as we know to the existing literature. We also consider joint models that incorporate information from both long-term trends and short-term variability in a longitudinal submodel. An application to the study of how follicle stimulating hormone (FSH) trajectories are related to the risk of developing severe hot flashes is considered.