Enhanced Working Correlation Structure Selection for the Modeling of Clustered Data using GEEs

Music City Center 

When modeling clustered data using generalized estimating equations, the selection of a proper correlation structure improves the efficiency of mean structure estimators. QIC and CIC are measures that can be used to perform working correlation structure selection. Both criteria assess the disparity between the robust estimator of the covariance matrix for the estimated mean parameters and a referent: specifically, the model-based covariance matrix estimator arising from the independence model. Such a referent is arguably suboptimal, since the independence working structure is usually inappropriate for clustered data. To address this issue, we propose new discrepancy measures that utilize the general working correlation structure as the referent, which should always be defensible provided that the correlation parameters can be accurately estimated. To facilitate the selection of a suitably parsimonious working correlation structure, we develop and implement a form of Occam's window based on bootstrapping that can be used in conjunction with the criteria.

bootstrapping

CIC

generalized estimating equations

model selection

Occam’s window

working correlation structure 

Biometrics Section