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Title: Clustering multi-outcome longitudinal data via finite mixtures of multivariate t linear mixed models Authors:  Wan-Lun Wang - Feng Chia University (Taiwan) [presenting]
Abstract: The issues of model-based clustering and classification of longitudinal data have received increasing attention in recent years. A finite mixture of multivariate $t$ linear mixed-effects model (FM-MtLMM) is presented for analyzing longitudinally measured multi-outcome data arisen from more than one heterogeneous sub-population. The motivation comes from a cohort study of patients with primary biliary cirrhosis (PBC), where the interest is in classifying new patients into two or more prognostic groups on the basis of their longitudinally observed bilirubin and albumin levels. The proposed FM-MtLMM offers robustness and flexibility to accommodate fat tails or atypical observations contained in one or several of the groups. An efficient alternating expectation conditional maximization (AECM) algorithm is employed for computing maximum likelihood estimates of parameters. Practical techniques for clustering of multivariate longitudinal data, estimation of random effects, and classification of future patients are also provided. The methodology is illustrated by analyzing Mayo Clinic PBC sequential data and a simulation study.