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There is a considerable amount of literature dealing with the problem of zero-inflated count data such as Zero Inflated Poisson (ZIP) or Zero Inflated Binomial (ZIB) mixture models, and their extension to clustered or longitudinal data structures [ 1- 7].
This model is an extension of linear regression for longitudinal data, and is specifically designed to handle correlated repeated measurements, missing data and dropouts.
The GEE approach is an extension of generalized linear models to the longitudinal data and handles both discrete and continuous phenotypes.
LMMs can be seen as an extension of latent class analysis for the analysis of longitudinal data, and they have been proposed for public health research in several studies (22– 22).
Random-effects models for longitudinal data.
Linear mixed models for longitudinal data.
Methods for analyzing longitudinal data.
Multilevel modeling of longitudinal data for psychotherapy researchers: II.
Includes current and 6-year longitudinal data.
Moreover, replicating his longitudinal data would be expensive and difficult to do.
The analysis relies on a longitudinal data set (CLHNS 1994-2005).
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