A selection model for longitudinal binary responses subject to non-ignorable attrition

Longitudinal studies collect information on a sample of individuals which is followed over time to analyze the effects of individual and time-dependent characteristics oil the observed response. These studies often Suffer from attrition: individuals drop out of the Study before its completion time and thus present incomplete data records. When the missing mechanism, once conditioned oil other (observed) variables, does not depend Oil Current (eventually unobserved) values of the response variable, the dropout mechanism is known to he ignorable. We propose a selection model extending semiparametric variance component models for longitudinal binary responses to allow for dependence between the missing data mechanism and the primary response process. The model is applied to a data set from a methadone maintenance treatment programme held in Sidney, 1986. Copyright (C) 2009 John Wiley & Sons, Ltd.