Missing values are not uncommon in longitudinal data studies.
Missingness could be due to withdrawal from the study (dropout) or intermittent.
The missing data mechanism is termed non-ignorable if the probability
of missingness depends on the unobserved (missing) observations. This
paper presents a model for continuous longitudinal data with non-ignorable
non-monotone missing values. Two separate models, for the response and
missingness, are assumed. The response is modeled as multivariate normal
whereas the binomial model for missingness process. Parameters in the
adopted model are estimated using the stochastic EM algorithm. The proposed
model (approach) is then applied to an example from the International
Breast Cancer Study Group.