  In longitudinal data a response variable is measured over time, or under
different conditions, for a cohort of individuals. In many situations all
intended measurements are not available which results in missing values. If the
missing value is never followed by an observed measurement, this leads to
dropout pattern. The missing values could be in the response variable, the
covariates or in both. The missingness mechanism is termed non-random when the
probability of missingness depends on the missing value and may be on the
observed values. In this case the missing values should be considered in the
analysis to avoid any potential bias. The aim of this article is to employ
multiple imputations (MI) to handle missing values in covariates using. The
selection model is used to model longitudinal data in the presence of
non-random dropout. The stochastic EM algorithm (SEM) is developed to obtain
the model parameter estimates in addition to the estimates of the dropout
model. The SEM algorithm does not provide standard errors of the estimates. We
developed a Monte Carlo method to obtain the standard errors. The proposed
approach performance is evaluated through a simulation study. Also, the
proposed approach is applied to a real data set.
