HIV Viral Dynamic Models With Censoring and Informative Dropouts

HIV viral dynamic models have received much interest in the literature in recent years. These models are useful for modeling the viral load trajectories during an anti-HIV treatment and for evaluating the efficacy of the treatment. In AIDS studies, patients may drop out of the study early due possibly to drug side-effects, and viral load measurements often have a lower limit of detection. Statistical analyses are therefore complicated by the censoring and dropouts in the data. We propose a joint likelihood method which addresses censoring and dropouts in a mixed effects model simultaneously. A real AIDS dataset is analyzed, and a simulation is conducted to evaluate the proposed method.