Bayesian multivariate logistic regression.
Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic distribution that can be used to construct a likelihood for multivariate logistic regression analysis of binary and categorical data. The model for individual outcomes has a marginal logistic structure, simplifying interpretation. We follow a Bayesian approach to estimation and inference, developing an efficient data augmentation algorithm for posterior computation. The method is illustrated with application to a neurotoxicology study.
O'Brien
Sean M
SM
Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA. obrien4@niehs.nih.gov
Dunson
David B
DB
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D005260:Female; D007306:Insecticides / Q000008:administration & dosage / Q000633:toxicity; D016015:Logistic Models; D008297:Male; D008390:Markov Chains; D008731:Methoxychlor / Q000008:administration & dosage / Q000633:toxicity; D009010:Monte Carlo Method; D009043:Motor Activity / Q000187:drug effects; D015999:Multivariate Analysis; D011247:Pregnancy; D051381:Rats
D007306:Insecticides; D008731:Methoxychlor