title: Bayesian inference for a  nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies.
abstract: Censored data are characteristics of many bioassays in HIV/AIDS studies where assays may not be sensitive enough to determine gradations in viral load determination among those below a detectable threshold. Not accounting for such left-censoring appropriately can lead to biased parameter estimates in most data analysis. To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew distributions. Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable. A Bayesian modeling approach via Markov Chain Monte Carlo (MCMC) algorithm is used to estimate model parameters. The proposed methods are illustrated using real data from an HIV/AIDS study.
authors.affiliation: University of South Florida, FL, USA.
authors.forename: Getachew
authors.initials: G
authors.lastname: Dagne
authors.forename: Yangxin
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000821:virology; D000465:Algorithms; D044966:Anti-Retroviral Agents / Q000494:pharmacology*; D001499:Bayes Theorem; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D019562:Viral Load / Q000187:drug effects
chemical_list: D044966:Anti-Retroviral Agents