Hi, I have a bivariate longitudinal dataset. As an example say, i have the data frame with column names var1 var2 Unit time trt (trt represents the treatment) Now suppose I want to fit a joint model of the form for the *i* th unit var1jk = alpha1 + beta1*timejk + gamma1* trtjk + delta1* timejk:trtjk + error1jk var2 = alpha2 + beta2*timejk + gamma2* trtjk + delta2* timejk:trtjk + error2jk where j index time and k index the treatment received Using indicator variables I have been able to fit and run the code for a bivariate model using unstructured covariance matrix. However, I want to fit a model for a structured variance covariance matrix. The error structure for the grouping unit is as follows sigma = ( sigma1 sigma12 ) ( sigma12 sigma2) sigma1, sigma2 and sigma12 are matrices with where sigma1 = sig1 * AR1(rho1) sigma2 = sig2* AR1(rho2) sigma12 = sig12 * AR1(rho12) My question is whether there is any method to fit such data using packages like gee or geepack (or may be any other package ) in R. The function genZcor() of geepack can be used to construct correlation but I have been unable to use it in the present context. Any help is greatly appreciated. Regards Souvik Banerjee Lecturer Department of statistics Memari College Burdwan India [[alternative HTML version deleted]]