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Simultaneous inference for generalized linear mixed models with informative dropout and missing covariates Wu, Kunling

Abstract

Generalized linear mixed effects models (GLMMs) are popular in many longitudinal studies. In these studies, however, missing data problems arise frequently, which makes statistical analyses more complicated. In this thesis, we propose an exact method and an approximate method for GLMMs with informative dropouts and missing covariates, and provide a unified approach for simultaneous inference. Both methods are implemented by Monte Carlo EM algorithms. The approximate method is based on Taylor series expansion, and it avoids sampling the random effects in the E-step. Thus, the approximate method may be computationally more efficient when the dimension of random effects is not small. We also briefly discuss other methods for accelerating the EM algorithms. To illustrate the proposed methods, we analyze two real datasets, a AIDS 315 dataset and a dataset from a parent bereavement project, using these methods. A simulation study is conducted to evaluate the performance of the proposed methods under various situations.

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