Bivariate random effect model using skew-normal distribution with application to HIV-RNA.

Published

Journal Article

Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random effects models are commonly used to model correlated data. Mostly in the longitudinal data setting we assume that the random effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential models and illustrate the procedure using a real data set from HIV study.

Full Text

Duke Authors

Cited Authors

  • Ghosh, P; Branco, MD; Chakraborty, H

Published Date

  • March 2007

Published In

Volume / Issue

  • 26 / 6

Start / End Page

  • 1255 - 1267

PubMed ID

  • 16998836

Pubmed Central ID

  • 16998836

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/sim.2667

Language

  • eng