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An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout
Author(s) -
Li Ning,
Daniels Michael J.,
Li Gang,
Elashoff Robert M.
Publication year - 2013
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201100107
Subject(s) - dropout (neural networks) , missing data , binary number , selection bias , random effects model , statistics , selection (genetic algorithm) , bayesian probability , binary data , computer science , longitudinal data , mathematics , econometrics , artificial intelligence , machine learning , data mining , medicine , meta analysis , arithmetic
We explore a Bayesian approach to selection of variables that represent fixed and random effects in modeling of longitudinal binary outcomes with missing data caused by dropouts. We show via analytic results for a simple example that nonignorable missing data lead to biased parameter estimates. This bias results in selection of wrong effects asymptotically, which we can confirm via simulations for more complex settings. By jointly modeling the longitudinal binary data with the dropout process that possibly leads to nonignorable missing data, we are able to correct the bias in estimation and selection. Mixture priors with a point mass at zero are used to facilitate variable selection. We illustrate the proposed approach using a clinical trial for acute ischemic stroke.