B0338
Title: Multivariate-t linear mixed models for longitudinal data with censored and intermittent missing responses
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Tsung-I Lin - National Chung Hsing University (Taiwan)
Abstract: Multivariate longitudinal data arising in clinical trials and medical studies often exhibit complex features such as censored responses, intermittent missing values, and atypical or outlying observations. The multivariate-$t$ linear mixed model (MtLMM) has been recognized as a powerful tool for the robust modeling of multivariate longitudinal data in the presence of potential outliers or fat-tailed noises. A generalization of MtLMM, called the MtLMM-CM, is presented to properly adjust for censorship due to detection limits of the assay and missingness embodied within multiple outcome variables recorded at irregular occasions. An expectation conditional maximization either (ECME) algorithm is developed to compute parameter estimates using the maximum likelihood (ML) approach. The asymptotic standard errors of the ML estimators of fixed effects are obtained by inverting the empirical information matrix according to Louis method. The proposed methodology is illustrated on a real dataset from HIV-AIDS studies and a simulation study under a variety of scenarios.
