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 present a model for continuous   for 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 < /p>