A0199
Title: Mixtures of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missingness
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Abstract: The multivariate $t$ nonlinear mixed-effects model (MtNLMM) has been shown to be effective for analyzing multi-outcome longitudinal data following nonlinear growth patterns with fat-tailed noises or potential outliers. The problem of clustering heterogeneous longitudinal profiles in a mixture framework of MtNLMM is considered. A finite mixture of multivariate $t$ nonlinear mixed model is proposed. This new model allows accommodating complex features of longitudinal data. Intermittent missing values frequently occur in the data collection process of multiple repeated measures. Under a missing at random (MAR) mechanism, a pseudo-data version of the alternating expectation conditional maximization (AECM) algorithm is developed to carry out maximum likelihood estimation and impute missing values simultaneously. The techniques for clustering incomplete multiple trajectories, recovery of missing responses, and estimation of random effects are also investigated. The utility of the proposed methods is illustrated through a simulation study and a real-data example coming from a study of pregnant women.
