Semiparametric nonlinear mixed-effects models are very flexible in modeling long-term HIV viral dynamics. In practice, statistical analyses are often complicated due to measurement errors and missing data in covariates and non-ignorable missing data in the responses. We consider likelihood methods which simultaneously address measurement error and missing data problems. A real dataset is analyzed in details, and a simulation study is conducted to evaluate the methods.