Journal
STATISTICS IN MEDICINE
Volume 27, Issue 30, Pages 6351-6366Publisher
WILEY
DOI: 10.1002/sim.3417
Keywords
asymptotes; autoregressive linear mixed effects models; covariance; dropouts; positive and negative syndrome scale (PANSS); schizophrenia
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We are interested in longitudinal data of a continuous response that show profiles with an initial sharp change and approaching asymptotes for each patient, and many patients drop out with a reason related to the response. In this paper, we focus on a model that assumes a dropout process is missing at random (MAR). In this dropout process, we can obtain consistent maximum likelihood estimators as long as both the mean and covariance structures are correctly specified. However, parsimonious covariance structures for the profiles approaching asymptotes are unclear. An autoregressive linear mixed effects model can express the profile with random individual asymptotes. We show that this model provides a new parsimonious covariance structure. The covariance structure at steady state is Compound symmetry and the other elements of the covariance depend on the measurement points. In simulation studies, the estimate of the asymptote is unbiased in MAR dropouts, but biased in non-ignorable dropouts. We also applied this model to actual schizophrenia trial data. Copyright (C) 2008 John Wiley & Sons, Ltd.
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