Alfo', Marco and Maruotti, Antonello
A selection model for longitudinal binary responses subject to non-ignorable attrition
Statistics in Medicine, 28, (19), . (doi:10.1002/sim.3604).
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Longitudinal studies collect information on a sample of individuals which is followed over time to analyze the effects of individual and time-dependent characteristics on 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 on other (observed) variables, does not depend on current (eventually unobserved) values of the response variable, the dropout mechanism is known to be 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.
|Digital Object Identifier (DOI):
||longitudinal binary responses, ar(1) variance components, non-ignorable dropouts, random effect-based dropout model, non-parametric maximum likelihood
||Statistics, Statistical Sciences Research Institute
|7 May 2009||e-pub ahead of print|
|30 August 2009||Published|
||10 Dec 2012 12:02
||17 Apr 2017 16:18
|Further Information:||Google Scholar|
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