title: Bayesian bent-cable growth mixture tobit models for longitudinal data with skewness and detection limit: application to AIDS studies.
abstract: This paper presents a new Bayesian methodology for identifying a transition period for the development of drug resistance to antiretroviral drug or therapy in HIV/AIDS studies or other related fields. Estimation of such a transition period requires an availability of longitudinal data where growth trajectories of a response variable tend to exhibit a gradual change from a declining trend to an increasing trend rather than an abrupt change. We assess this clinically important feature of the longitudinal HIV/AIDS data using the bent-cable framework within a growth mixture Tobit model. To account for heterogeneity of drug resistance among subjects, the parameters of the bent-cable growth mixture Tobit model are also allowed to differ by subgroups (subpopulations) of patients classified into latent classes on the basis of trajectories of observed viral load data with skewness and left-censoring. The proposed methods are illustrated using real data from an AIDS clinical study. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Department of Epidemiology & Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL, 33612, U.S.A.. gdagne@health.usf.edu.
authors: Getachew A
authors: GA
authors: Dagne
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000821:virology; D001499:Bayes Theorem; D015658:HIV Infections; D006801:Humans; D057230:Limit of Detection; D008137:Longitudinal Studies; D015233:Models, Statistical; D019562:Viral Load
keywords: Bayesian inference; change-points; mixed-effects models; mixture distribution; skew distribution