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Abstract:
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Longitudinal data are often collected in medical and biomedical applications where measurements on more than one response can be taken from a given subject repeatedly overtime. For some problems, these multivariate profiles need to be modeled jointly in order to get insight on the joint evolution and/or association of these responses over time. In practice, these multiple longitudinal outcomes may have zeros that need to be accounted for in the analysis. In this paper, we propose a multivariate zero-inflated joint model to analyze multivariate hierarchical semicontinuous data characterized by excess zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components.
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