<p>Missing data and measurement errors in covariates are common in many longitudinal studies. In this paper, we propose and explore an approximate maximum likelihood method in the framework of the linear mixed model for longitudinal data with missing responses and covariate measurement errors. To adjust for the measurement error in a covariate, we adopt a regression calibration method. Also, to deal with nonignorable missing data, we adopt a Monte Carlo expectation-maximization method that approximates the maximum likelihood estimators of the model parameters. The finite-sample properties of the proposed estimators are studied using Monte Carlo simulations. An application is provided using actual data obtained from a health survey.</p>