title: A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data.
abstract: The method of generalized estimating equations (GEE) models the association between the repeated observations on a subject with a patterned correlation matrix. Correct specification of the underlying structure is a potentially beneficial goal, in terms of improving efficiency and enhancing scientific understanding. We consider two sets of criteria that have previously been suggested, respectively, for selecting an appropriate working correlation structure, and for ruling out a particular structure(s), in the GEE analysis of longitudinal studies with binary outcomes. The first selection criterion chooses the structure for which the model-based and the sandwich-based estimator of the covariance matrix of the regression parameter estimator are closest, while the second selection criterion chooses the structure that minimizes the weighted error sum of squares. The rule out criterion deselects structures for which the estimated correlation parameter violates standard constraints for binary data that depend on the marginal means. In addition, we remove structures from consideration if their estimated parameter values yield an estimated correlation structure that is not positive definite. We investigate the performance of the two sets of criteria using both simulated and real data, in the context of a longitudinal trial that compares two treatments for major depressive episode. Practical recommendations are also given on using these criteria to aid in the efficient selection of a working correlation structure in GEE analysis of longitudinal binary data.
authors.affiliation: Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA 19034, USA. jshults@mail.med.upenn.edu
authors.forename: Justine
authors.initials: J
authors.lastname: Shults
authors.forename: Wenguang
authors.initials: W
authors.lastname: Sun
authors.forename: Xin
authors.initials: X
authors.lastname: Tu
authors.forename: Hanjoo
authors.initials: H
authors.lastname: Kim
authors.forename: Jay
authors.initials: J
authors.lastname: Amsterdam
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Hilbe
authors.forename: Thomas
authors.initials: T
authors.lastname: Ten-Have
mesh_terms: D018687:Antidepressive Agents, Second-Generation / Q000627:therapeutic use; D001699:Biometry; D003198:Computer Simulation; D003511:Cyclohexanols / Q000627:therapeutic use; D003865:Major Depressive Disorder / Q000188:drug therapy; D006801:Humans; D016014:Linear Models; D018020:Lithium Compounds / Q000627:therapeutic use; D008137:Longitudinal Studies; D011446:Prospective Studies; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012044:Regression Analysis; D000069470:Venlafaxine Hydrochloride
chemical_list: D018687:Antidepressive Agents, Second-Generation; D003511:Cyclohexanols; D018020:Lithium Compounds; D000069470:Venlafaxine Hydrochloride