Simple pattern-mixture models for longitudinal data with missing observations: Analysis of urinary incontinence data

In longitudinal studies each subject is observed at several different times . Longitudinal studies are rarely balanced and complete due to occurrence o f missing data. Little proposed pattern-mixture models for the analysis of incomplete multivariate normal data. Later, Little proposed an approach to modelling the drop-out mechanism based on the pattern-mixture models. We ad vocate the pattern-mixture models for analysing the longitudinal data with binary or Poisson responses in which the generalized estimating equations f ormulation of Liang and Zeger is sensible. The proposed method is illustrat ed with a real data set. Copyright (C) 1999 John Wiley & Sons, Ltd.