An EM algorithm fits a state-space formulation of the longitudinal regression model in which a continuous response depends on the lagged response and both time-dependent and time-independent covariates. The baseline response depends only on covariates. The model handles both missing data and Gaussian measurement error on both response and continuous covariates. TheEstep uses the Kalman filter and associated filtering algorithms to update the unknown true response and predictor series for the observed data. TheMstep uses standard closed-form Gaussian results. Standard errors come from the supplemented EM (SEM) algorithm. The model accurately fits 6 years of pulmonary function measurements on 158 children with many missing observations.