A PRESS statistic for working correlation structure selection in generalized estimating equations

ABSTRACT

Generalized estimating equations (GEE) is one of the most commonly used methods for regression analysis of longitudinal data, especially with discrete outcomes. The GEE method accounts for the association among the responses of a subject through a working correlation matrix and its correct specification ensures efficient estimation of the regression parameters in the marginal mean regression model. This study proposes a predicted residual sum of squares (PRESS) statistic as a working correlation selection criterion in GEE. A simulation study is designed to assess the performance of the proposed GEE PRESS criterion and to compare its performance with its counterpart criteria in the literature. The results show that the GEE PRESS criterion has better performance than the weighted error sum of squares SC criterion in all cases but is surpassed in performance by the Gaussian pseudo-likelihood criterion. Lastly, the working correlation selection criteria are illustrated with data from the Coronary Artery Risk Development in Young Adults study.

KEYWORDS:

• Correlation structure
• deletion diagnostics
• longitudinal discrete responses
• unbalanced longitudinal data
• unequally spaced longitudinal data

Acknowledgements

This study was initiated while Gul Inan was visiting Department of Biostatistics, University of North Carolina-Chapel Hill, USA. She would like to thank to the Scientific and Technological Research Council of Turkey (TUBITAK) for funding her post-doctoral studies in USA. This study was presented at the 38th Annual Conference of the International Society for Clinical Biostatistics (ISCB) in Vigo, Spain in 2017 through a travel grant program of the ISCB conference. Authors would also like to thank the editor and two anonymous reviewers for their constructive comments which improved the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors