Abstract:
Hepatitis C virus (HCV) coinfection has become one of the most
challenging clinical situations to manage in HIV-infected patients.
Recently the effect of HCV coinfection on HIV dynamics following
initiation of highly active antiretroviral therapy (HAART) has drawn
considerable attention. Post-HAART HIV dynamics are commonly
studied in short-term clinical trials with frequent data collection de-
sign. For example, the elimination process of plasma virus during
treatment is closely monitored with daily assessments in viral dy-
namics studies of AIDS clinical trials. In this article instead we use
infrequent cohort data from long-term natural history studies and
develop a model for characterizing post-HAART HIV dynamics and
their associations with HCV coinfection. Specifically, we propose a
joint model for doubly interval-censored data for the time between
HAART initiation and viral suppression, and the longitudinal CD4
count measurements relative to the viral suppression. Inference is ac-
complished using a fully Bayesian approach. Doubly interval-censored
data are modeled semiparametrically by Dirichlet process priors and
Bayesian penalized splines are used for modeling population-level and
individual-level mean CD4 count profiles. We use the proposed meth-
ods and data from the HIV Epidemiology Research Study (HERS) to
investigate the effect of HCV coinfection on the response to HAART.
