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A measure of partial association for generalized estimating equationsSundar Natarajan, Department of Medicine, New York University School of Medicine, and the VA New York Harbor Healthcare System, US. E-mail: sundar.natarajan{at}med.nyu.edu
Stuart Lipsitz, Division of General Internal Medicine, Brigham and Women's Hospital, US.
Michael Parzen, Goizueta Business School, Emory University, US.
Stephen Lipshultz, Department of Pediatrics, University of Miami School of Medicine, US. In a regression setting, the partial correlation coefficient is often used as a measure of ‘standardized’ partial association between the outcome y and each of the covariates in x' = [x1, . . . , xK ]. In a linear regression model estimated using ordinary least squares, with y as the response, the estimated partial correlation coefficient between y and xk can be shown to be a monotone function, denoted f (z), of the Z–statistic for testing if the regression coefficient of xk is 0. When y is non–normal and the data are clustered so that y and x are obtained from each member of a cluster, generalized estimating equations are often used to estimate the regression parameters of the model for y given x. In this paper, when using generalized estimating equations, we propose using the above transformation f (z) of the GEE Z–statistic as a measure of partial association. Further, we also propose a coefficient of determination to measure the strength of association between the outcome variable and all of the covariates. To illustrate the method, we use a longitudinal study of the binary outcome heart toxicity from chemotherapy in children with leukaemia or sarcoma.
Key Words: coefficient of determination • longitudinal data • repeated measures
Statistical Modelling, Vol. 7, No. 2,
175-190 (2007) |
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