title: An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout.
abstract: 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.
authors.affiliation: Department of Biomathematics, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA. nli@biomath.ucla.edu
authors.forename: Ning
authors.initials: N
authors.lastname: Li
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Daniels
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Robert M
authors.initials: RM
authors.lastname: Elashoff
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002545:Brain Ischemia / Q000150:complications; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D010960:Plasminogen Activators / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic; D020521:Stroke / Q000150:complications / Q000188:drug therapy
chemical_list: D010960:Plasminogen Activators