title: Working-correlation-structure identification in generalized estimating equations.
abstract: Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well-known criterion of QIC for selecting a working correlation structure, and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads us to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study.
authors.affiliation: lyhin@netvigator.com
authors.forename: Lin-Yee
authors.initials: LY
authors.lastname: Hin
authors.forename: You-Gan
authors.initials: YG
authors.lastname: Wang
mesh_terms: D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D012044:Regression Analysis