Serial correlation in unequally spaced longitudinal data

Abstract

Serial correlation in the within subject error structure in longitudinal data with unequally spaced observations is modelled using continuous time autoregressive moving averages. The models considered have both fixed and random effects in addition to serially correlated within subject errors. Two approaches are presented for calculating the exact likelihood for a model when the errors are Gaussian. The first calculates the covariance matrices for each subject for assumed values of the unknown parameters and estimates the fixed parameters by weighted least squares. The second uses a state space model and the Kalman filter to calculate the exact likelihood. Both methods involve the use of complex arithmetic. Nonlinear optimization is used to obtain maximum likelihood estimates of the parameters.