A Bayesian approach for estimating antiviral efficacy in HIV dynamic models

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.