Analysis of multivariate longitudinal data using quasi-least squares

In this paper we consider the analysis of multivariate longitudinal data assuming a scale multiple of Kronecker product correlation structure for the covariance matrix of the observations on each subject. The method used for the estimation of the parameters is the quasi-least squares method developed in the following three papers: Chaganty (J. Statist. Plann. Inference 63 (1997) 39), Shults and Chaganty (Biometrics 54 (1998) 1622) and Chaganty and Shults (J. Statist. Plann. Inference 76 (1999) 145). We show that the estimating equations for the correlation parameters in the quasi-least-squares method are optimal unbiased estimating equations if the data is from a normal population. An algorithm for computing the estimates is provided and implemented on a real life data set. The asymptotic joint distribution of the estimators of the regression and correlation parameters is derived and used for testing a linear hypothesis on the regression parameters. (C) 2002 Elsevier Science B.V. All rights reserved.