Long-term HIV dynamic models incorporating drug adherence and resistance to treatment for prediction of virological responses

Yangxin Huang

Computational Statistics & Data Analysis, 2008, vol. 52, issue 7, 3765-3778

Abstract: Long-term therapy with antiretroviral (ARV) agents in HIV-infected patients often results in failure to suppress the viral load. Imperfect adherence and drug susceptibility to prescribed antiviral drugs are important factors explaining the resurgence of virus. A better understanding of the factors responsible for the virological failure is critical for the development of new treatment strategies. In this paper, we develop a mechanism-based reparameterized differential equation model for characterizing long-term viral dynamics with ARV therapy. In this model we directly incorporate drug susceptibility and drug adherence (measured by medication event monitoring system (MEMS) and questionnaires) into a function of treatment efficacy. A Bayesian nonlinear mixed-effects modeling approach is investigated for estimating dynamic parameters by fitting the model to viral load data from an AIDS clinical trial. The effects of drug adherence interaction with drug resistance-based models are compared using (i) the sum of the squared residual (SSR) from individual subjects and (ii) the deviance information criterion (DIC), a Bayesian version of the classical deviance for model assessment, designed from complex hierarchical model settings. The results indicate that the drug adherence combined with confounding factor, drug resistance in viral dynamic modeling significantly predict virologic responses. Our study suggests that long-term reparameterized dynamic models are powerful and effective in establishing a relationship of antiviral responses with drug adherence and susceptibility.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00002-9
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:52:y:2008:i:7:p:3765-3778

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().