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A measure of partial association for generalized estimating equations. (English) Zbl 07257678
Summary: 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 $$x' = [x_1, \ldots, x_K]$$. In a linear regression model estimated using ordinary least squares, with $$y$$ as the response, the estimated partial correlation coefficient between $$y$$ and $$x_k$$ can be shown to be a monotone function, denoted f (z), of the $$Z$$-statistic for testing if the regression coefficient of $$x_k$$ 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.
##### MSC:
 62 Statistics
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