  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 design. For example, the elimination process of plasma virus during
treatment is closely monitored with daily assessments in viral dynamics 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
accomplished 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 methods and data from the HIV Epidemiology
Research Study (HERS) to investigate the effect of HCV coinfection on the
response to HAART.
