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A selection model for longitudinal binary responses subject to non‐ignorable attrition
Author(s) -
Alfò Marco,
Maruotti Antonello
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3604
Subject(s) - attrition , missing data , econometrics , computer science , binary data , selection (genetic algorithm) , data set , selection bias , statistics , set (abstract data type) , dropout (neural networks) , variance (accounting) , longitudinal data , binary number , mathematics , data mining , artificial intelligence , medicine , economics , arithmetic , dentistry , accounting , machine learning , programming language
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. Copyright © 2009 John Wiley & Sons, Ltd.