A Bayesian approach for estimating antiviral efficacy in HIV dynamic models

Yangxin Huang and Hulin Wu

Journal of Applied Statistics, 2006, vol. 33, issue 2, 155-174

Abstract: The study of HIV dynamics is one of the most important developments in recent AIDS research. It has led to a new understanding of the pathogenesis of HIV infection. Although important findings in HIV dynamics have been published in prestigious scientific journals, the statistical methods for parameter estimation and model-fitting used in those papers appear surprisingly crude and have not been studied in more detail. For example, the unidentifiable parameters were simply imputed by mean estimates from previous studies, and important pharmacological/clinical factors were not considered in the modelling. In this paper, a viral dynamic model is developed to evaluate the effect of pharmacokinetic variation, drug resistance and adherence on antiviral responses. In the context of this model, we investigate a Bayesian modelling approach under a non-linear mixed-effects (NLME) model framework. In particular, our modelling strategy allows us to estimate time-varying antiviral efficacy of a regimen during the whole course of a treatment period by incorporating the information of drug exposure and drug susceptibility. Both simulated and real clinical data examples are given to illustrate the proposed approach. The Bayesian approach has great potential to be used in many aspects of viral dynamics modelling since it allow us to fit complex dynamic models and identify all the model parameters. Our results suggest that Bayesian approach for estimating parameters in HIV dynamic models is flexible and powerful.

Keywords: Bayesian mixed-effects models; drug efficacy; drug resistance; HIV; MCMC; viral dynamics (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760500250552 (text/html)
Access to full text is restricted to subscribers.

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:taf:japsta:v:33:y:2006:i:2:p:155-174

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760500250552

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().