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Activity Number: 134
Type: Contributed
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #309467
Title: Bayesian Bivariate Linear Mixed-Effects Models with Skewed Distributions, with Application to AIDS Studies
Author(s): Yangxin Huang*+ and Ren Chen
Companies: University of South Florida and University of South Florida
Keywords: Bayesian analysis ; bivariate linear mixed-effects models ; skew-normal independent distributions ; longitudinal data ; time-varying drug efficacy

Bivariate correlated data often encountered in epidemiological and clinical research are routinely analyzed under a LME model with normality assumptions for the random-effects and within-subject errors. However, those analyses might not provide robust inference when the normality assumptions are questionable if the data set particularly exhibits skewness and heavy tails. This paper develops a Bayesian approach to bivariate LME models replacing the Gaussian assumptions for the random terms with skew-normal/independent (SNI) distributions. The SNI distribution is an attractive class of asymmetric heavy-tailed parametric structure which includes the skew-normal, skew-t, skew-slash and skew-contaminated normal distributions as special cases. We assume that the random-effects and the within-subject (random) errors, respectively, follow multivariate SNI and normal/independent (NI) distributions, which provide an appealing robust alternative to the symmetric normal distribution in a BLME model framework. The method is exemplified through an application to an AIDS data set to compare potential models with different distribution specifications and clinically important findings are reported.

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