Wednesday 21 Nov 2012: Within-Subject Variance as a Predictor of Health Outcomes
Professor Michael Elliott - Ann Arbor University, USA
Rolle Building Room 213, Plymouth University 15:00-16:00
Growth Mixture Models (GMMs) are used to model heterogeneity in longitudinal trajectories. GMMs assume that each subject's growth curve, characterized by random coefficients in mixed effects models, belongs to an underlying latent cluster with a cluster-specific mean profile. Within-subject variability is typically treated as a nuisance and assumed to be non-differential. Elliott (2007) extended the idea of modeling random effects as finite mixtures as in GMMs into the variance structure setting, where underlying `clusters'
of within-subject variabilities were related to the health outcome of interest while the subject-specific trajectories were treated entirely as nuisance and modeled by penalized
smoothing splines. We extend these ideas by allowing `heterogeneities' (i.e., clusters) in both the growth curves and within-subject variabilities and develop a method that simultaneously examines the association between the underlying mean growth profile and the variance clusters with a cross-sectional binary health outcome. We consider an applications to predict onset of senility in a population sample of older adults using memory test scores and to predict severe hot flushes using the hormone levels collected over time for women in menopausal transition.