A Nonlinear Mixed-Effects Model for Multivariate Longitudinal Data with Dropout with Application to HIV Disease Dynamics

The main challenge in biomedical and clinical studies which involve collection of longitudinal data is the premature withdrawal of the subjects from the study resulting in incomplete data. Standard statistical analysis approaches usually give biased estimates of the model parameters if the mechanisms that led to dropout are ignored. In this discussion, we consider nonlinear mixed-effects models for multivariate longitudinal data in the presence of subject dropout. We present techniques for estimation of model parameters. These procedures are applied to estimate the parameters in the HIV dynamic system using routine observational data from an HIV clinic.