We present a new joint modeling approach for correlated data analysis.Utilizing Gaussian copulas,we develop a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous,discrete and mixed correlated outcomes.This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients.