Missing data commonly arise during longitudinal measurements. Dropout is a particular
troublesome type of missingness because inference after the dropout occasion
is effectively precluded at the level of the individual without substantial assumptions.
If missingness, such as dropout, is related to the unobserved outcome variables, then
parameter estimates derived from models which ignore the missingness will be biased.
For example, a treatment effect may appear less substantial if poor-performing
subjects tend to withdraw from the study. In a general sense, missing data lead
to scenarios in which the empirical distribution of observed data is lacking nominal
coverage in some areas. Little (1993) proposed a general pattern-mixture model approach
in which the moments of the full data distribution were estimated as a finite
mixture across the various missing-data patterns. These models and their extensions
are flexible and may be estimated using wildly available mixed-modeling software in
some special cases. The purpose of this work is to review the relevant missing-data
literature and to examine the viability of random-coeffcient pattern-mixture models
as an option for analysts seeking unbiased inference for longitudinal data subject to