A Bayesian Approach for Estimating Antiviral Efficacy
in HIV Dynamic Models
Yangxin Huang
Frontier Science & Technology Research Foundation
Harvard School of Public Health, Chestnut Hill, MA, 02467
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 (nonlinear least squares, for example) for
parameter estimation and model-fitting used in those papers appear surprisingly crude
and have not been studied in more details. In this talk, a viral dynamic model is
developed to evaluate the effect of pharmacokinetic variation, drug resistance and
adherence on antiviral response. In the context of this model describing HIV infection,
we investigate a Bayesian modeling approach under a nonlinear hierarchical model
framework. In particular, our modeling strategy allows us to estimate time-varying
antiviral efficacy of a regimen during the whole course of treatment period by
incorporating the information of drug exposure and drug sensitivity. Both simulation and
real clinical data examples are given to illustrate the proposed approach. The Bayesian
approach involves assumptions of probability distributions for model parameters prior to
an analysis being performed, allowing the fitting of complex models and enabling
analysis of all of the model parameters, and has great potential to be used in many aspects
of viral dynamics modeling. It is suggested that Bayesian approach for estimating
parameters in HIV dynamic models is more flexible and powerful than the nonlinear least
squares method.