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A Hybrid Model for Nonignorable Dropout in Longitudinal Binary Responses
Author(s) -
Wilkins Kenneth J.,
Fitzmaurice Garrett M.
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00402.x
Subject(s) - dropout (neural networks) , computer science , covariate , binary data , inference , binary number , econometrics , model selection , selection (genetic algorithm) , maximum likelihood , statistics , machine learning , artificial intelligence , mathematics , arithmetic
Summary This article presents a likelihood‐based method for handling nonignorable dropout in longitudinal studies with binary responses. The methodology developed is appropriate when the target of inference is the marginal distribution of the response at each occasion and its dependence on covariates. A “hybrid” model is formulated, which is designed to retain advantageous features of the selection and pattern‐mixture model approaches. This formulation accommodates a variety of assumed forms of nonignorable dropout, while maintaining transparency of the constraints required for identifying the overall model. Once appropriate identifying constraints have been imposed, likelihood‐based estimation is conducted via the EM algorithm. The article concludes by applying the approach to data from a randomized clinical trial comparing two doses of a contraceptive.