title: Bivariate random effect model using skew-normal distribution with application to HIV-RNA.
abstract: 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.
authors.affiliation: Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303-3083, USA. pghosh@mathstat.gsu.edu
authors.forename: Pulak
authors.initials: P
authors.lastname: Ghosh
authors.forename: Marcia D
authors.initials: MD
authors.lastname: Branco
authors.forename: Hrishikesh
authors.initials: H
authors.lastname: Chakraborty
mesh_terms: D001499:Bayes Theorem; D015658:HIV Infections / Q000235:genetics*; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D016011:Normal Distribution; D012367:RNA, Viral
chemical_list: D012367:RNA, Viral