HIV dynamics and natural history studies: Joint modeling with doubly interval-censored event time and infrequent longitudinal data
Li Su, Joseph W. Hogan

TL;DR
This paper develops a Bayesian joint modeling approach for analyzing infrequent, doubly interval-censored event times and longitudinal CD4 count data in HIV studies, especially considering HCV coinfection effects.
Contribution
It introduces a novel Bayesian joint model using Dirichlet process priors and penalized splines for infrequent, doubly interval-censored data in HIV research.
Findings
HCV coinfection affects HIV viral suppression dynamics
The model effectively analyzes long-term cohort data
Bayesian methods provide flexible inference for complex data structures
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 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…
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