We consider longitudinal clinical data for HIV patients undergoing treatment interrupt
ions. We use a nonlinear dynamical mathematical model in attempts to fit individual pa
tient data. A statistically-based censored data method is combined with inverse proble
m techniques to estimate dynamic parameters. The predictive capabilities of this appro
ach are demonstrated by comparing simulations based on estimation of parameters using
only half of the longitudinal observations to the full longitudinal data sets.