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An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout

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...

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Autors principals: Li, Ning, Daniels, Michael J., Li, Gang, Elashoff, Robert M.
Format: Artigo
Idioma:Inglês
Publicat: 2012
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Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC3855104/
https://ncbi.nlm.nih.gov/pubmed/23124889
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/bimj.201100107
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