Outlier detection method in GEEs

• Published in: Biometrical journal Vol. 56; no. 5; pp. 838 - 850
• Main Authors: Pardo, María del Carmen, Hobza, Tomáš
• Format: Journal Article
• Language: English
• Published: Germany Blackwell Publishing Ltd 01-09-2014; Wiley - VCH Verlag GmbH & Co. KGaA
• Subjects: Algorithms; Computer Simulation; Data Interpretation, Statistical; Generalized estimating equations (GEEs); Longitudinal data; Longitudinal Studies; Models, Theoretical; Outlier detection; Score test statistic; Outlier detection; Generalized estimating equations (GEEs); Longitudinal data; Score test statistic
• ISSN: 0323-3847; 1521-4036
• DOI: 10.1002/bimj.201300149

The generalized estimating equations (GEEs) method has become quite useful in modeling correlated data. However, diagnostic tools to check that the selected final model fits the data as accurately as possible have not been explored intensively. In this paper, an outlier detection technique is developed based on the use of the “working” score test statistic to test an appropriate mean‐shift model in the context of longitudinal studies based on GEEs. Through a simulation study it has been shown that this method correctly singled out the outlier when the data set had a known outlier. The method is applied to a set of data to illustrate the outlier detection procedure in GEEs.