title: Bayesian analysis of piecewise growth mixture models with skew-t distributions: Application to AIDS studies.
abstract: A major problem in HIV/AIDS studies is the development of drug resistance to antiretroviral (ARV) drug or therapy. Estimating the time at which such drug resistance would develop is usually sought. The goal of this article is to perform this estimation by developing growth mixture models with change-points and skew-t distributions based on longitudinal data. For such data, following ARV treatment, the profile of each subject's viral load tends to follow a 'broken stick' like growth trajectory, indicating multiple phases of decline and increase in viral loads. These multiple phases with multiple change-points are captured by subject-specific random parameters of growth curve models. To account for heterogeneity of drug resistance among subjects, the change-points are also allowed to differ by subgroups (subpopulations) of patients classified into latent classes on the basis of trajectories of observed viral loads. The proposed methods are illustrated using real data from an AIDS clinical study.
authors.affiliation: a Department of Epidemiology & Biostatistics , College of Public Health, University of South Florida , Tampa , Florida , USA.
authors.forename: Getachew A
authors.initials: GA
authors.lastname: Dagne
authors.affiliation: b Robert Stempel College of Public Health & Social Work , Florida International University , Miami , Florida , USA.
authors.forename: Boubakari
authors.initials: B
authors.lastname: Ibrahimou
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy*; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D015233:Models, Statistical; D019562:Viral Load
keywords: Bayesian inference; mixed-effects models; mixture distribution; skew distribution