Bayesian multivariate logistic regression

Biometrics. 2004 Sep;60(3):739-46. doi: 10.1111/j.0006-341X.2004.00224.x.


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.

MeSH terms

  • Algorithms
  • Animals
  • Bayes Theorem*
  • Biometry
  • Female
  • Insecticides / administration & dosage
  • Insecticides / toxicity
  • Logistic Models*
  • Male
  • Markov Chains
  • Methoxychlor / administration & dosage
  • Methoxychlor / toxicity
  • Monte Carlo Method
  • Motor Activity / drug effects
  • Multivariate Analysis
  • Pregnancy
  • Rats


  • Insecticides
  • Methoxychlor