Modeling HIV dynamics using unified mixed-effects models

Abstract

Studies of HIV dynamics play a crucial role in understanding the pathogenesis of HIV infection and in evaluating antiviral therapies. To accelerate AIDS clinical trials, viral load (HIV-1 RNA copies) is now used as a surrogate marker. The time range of collecting the viral load data may be divided into three successive stages. There are several existing models for the first and second stage viral load data. Recently a semiparametric nonlinear mixed-effects (NLME) model has been proposed for the complete viral load data which include the third stage viral load data, i.e., the data of those patients who fail the therapies. An important and challenging problem is if the existing models that are good only for the first one or two-stage viral load data can be generalized so that they are applicable for the complete viral load data. Another question is which model is the most preferred. In this paper, we propose a unified mixed-effects model, which models population characteristics and individual variations semiparametrically, so that all existing and recently proposed models are its special cases. We employ a basis-based approach to solve this model. We also discuss and generalize the existing and recently proposed models, including uniexponential, biexponential, multiexponential and biexponential semiparametric models. We employed AIC and BIC to compare these models and found that for the complete viral load data, the recently proposed biexponential semiparametric NLME model is the best. Copyright © 2010 by American Sciences Press, Inc.