A measure of partial association for generalized estimating equations

Sundar Natarajan, Stuart Lipsitz, Michael Parzen, Stephen Lipshultz

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)175-190
Number of pages16
JournalStatistical Modelling
Issue number2
StatePublished - Jul 2007
Externally publishedYes


  • Coefficient of determination
  • Longitudinal data
  • Repeated measures

ASJC Scopus subject areas

  • Statistics and Probability


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