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Title: Bayesian analysis of multivariate linear mixed models with censored and missing responses Authors:  Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Abstract: Multivariate longitudinal data usually exhibit complex features such as the presence of censored responses due to detection limits of the assay and unavoidable missing values arising when participants make irregular visits that lead to intermittently recorded characteristics. A generalization of the multivariate linear mixed model constructed by taking impacts of censored and intermittent missing responses into account simultaneously, which is named the MLMM-CM, has been recently proposed for more precisely analyzing such kinds of data. The aim is at presenting a fully Bayesian approach to the MLMM-CM for addressing the uncertainties of censored and missing responses as well as unknown parameters. Bayesian computational techniques based on the inverse Bayes formulas (IBF) coupled with the Gibbs scheme are developed for carrying out posterior inference of the model. The proposed methodology is illustrated through a simulation study and a real-data example from the Adult AIDS Clinical Trials Group 388 study. Numerical results show empirically that the proposed Bayesian methodology performs satisfactorily and offers reliable posterior inference.