Bayesian analysis of multivariate mixed models for a prospective cohort study using skew-elliptical distributions.


Journal Article

Classical multivariate mixed models that acknowledge the correlation of patients through the incorporation of normal error terms are widely used in cohort studies. Violation of the normality assumption can make the statistical inference vague. In this paper, we propose a Bayesian parametric approach by relaxing this assumption and substituting some flexible distributions in fitting multivariate mixed models. This strategy allows for the skewness and the heavy tails of error-term distributions and thus makes inferences robust to the violation. This approach uses flexible skew-elliptical distributions, including skewed, fat, or thin-tailed distributions, and imposes the normal model as a special case. We use real data obtained from a prospective cohort study on the low back pain to illustrate the usefulness of our proposed approach.

Full Text

Duke Authors

Cited Authors

  • Kazemi, I; Mahdiyeh, Z; Mansourian, M; Park, JJ

Published Date

  • July 2013

Published In

Volume / Issue

  • 55 / 4

Start / End Page

  • 495 - 508

PubMed ID

  • 23609779

Pubmed Central ID

  • 23609779

Electronic International Standard Serial Number (EISSN)

  • 1521-4036

Digital Object Identifier (DOI)

  • 10.1002/bimj.201100208


  • eng

Conference Location

  • Germany