A bidimensional finite mixture model for longitudinal data subject to dropout

@article{Spagnoli2018ABF,
  title={A bidimensional finite mixture model for longitudinal data subject to dropout},
  author={Alessandra Spagnoli and Maria Francesca Marino and Marco Alf{\`o}},
  journal={Statistics in Medicine},
  year={2018},
  volume={37},
  pages={2998 - 3011}
}
In longitudinal studies, subjects may be lost to follow up and, thus, present incomplete response sequences. When the mechanism underlying the dropout is nonignorable, we need to account for dependence between the longitudinal and the dropout process. We propose to model such a dependence through discrete latent effects, which are outcome‐specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a probability matrix to describe the… 
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