“School of Biological Sciences”
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| Paper IPM / Biological Sciences / 13372 |
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| Abstract: | |||||
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Abstract: When the nature of a data set comes from a skew distribution, the use of usual Gaussian mixed effect model
can be unreliable. In recent years, skew-normal mixed effect models have been used frequently for longitudinal data
modeling in many biomedical studies. These models are flexible for considering skewness of the longitudinal data. In this
paper, a shared parameter model is considered for simultaneously analysing nonignorable missingness and skew
longitudinal outcomes. A Bayesian approach using Markov Chain Monte Carlo is adopted for parameter estimation.
Some simulation studies are performed to investigate the performance of the proposed methods. The proposed methods
are applied for analyzing an AIDS data set, where CD4 count measurements are gathered as longitudinal outcomes. In
these data CD4 counts measurements are severely skew. In application section, different structures of skew-normal
distribution assumptions for random effects and errors are considered where deviance information
criterion is used for
model comparison.
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