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Title: Multivariate linear mixed models with censored and nonignorable missing outcomes Authors:  Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Tsung-I Lin - National Chung Hsing University (Taiwan)
Abstract: The analysis of multivariate longitudinal data could encounter some complications due to censorship induced by detection limits of the assay and non-response occurring when participants missed scheduled visits intermittently or discontinued participation. A generalization of the multivariate linear mixed model is established that can accommodate censored responses and nonignorable missing outcomes simultaneously. To account for the nonignorable missingness, the selection approach which decomposes the joint distribution as a marginal distribution for the primary outcome variables and a model describing the missing process conditional on the hypothetical complete data is used. A computationally feasible Monte Carlo expectation conditional maximization (MCECM) algorithm is developed for parameter estimation with the maximum likelihood (ML) method. Furthermore, a general information-based approach is presented to assess the variability of ML estimators. The techniques for the prediction of censored responses and the imputation of missing outcomes are also discussed. The methodology is motivated and exemplified by a real dataset concerning HIV-AIDS clinical trials. A simulation study is conducted to examine the performance of the proposed method compared with other traditional approaches.