Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable

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Missing data methods in longitudinal studies: a review (2009, background): “These issues as well as other modeling and elicitation issues are discussed in Ibrahim et al (2001) (Chapter 8), [Ibrahim et al (2002), Chen et al (2002), Chen, Ibrahim, Shao (2004), Chen, Ibrahim, Shao (2006), and Ibrahim Chen, Kim (2008).”
Smoothing and mixed models (2003, methodology): “Particularly noteworthy in the context of this article are likelihood-based models that account for missing data; for example, Little and Rubin, (1987), Ibrahim, (1990) and Ibrahim, Chen and Lipsitz (2001) .”
Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data (2006, methodology): “where bi is the vector of latent random effects with distribution N [0, � ], Ibrahim et al. (2001) suggested using the logistic regression that depends on yi but not bi. The assumption that the missingness mechanism does not depend on bi is important in their likelihood-based approach with the EM ”
Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data (2015, methodology): “Note that Ibrahim, Chen, and Lipsitz (2001) actually made the same assumption, that is, assumption (2) with Z = Y0.”