title: Working covariance model selection for generalized estimating equations.
abstract: We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice.
authors: Harvard Medical School, Channing Laboratory, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, USA. stvjc@channing.harvard.edu
authors: Vincent J
authors: VJ
authors: Carey
authors: You-Gan
authors: YG
authors: Wang
mesh_terms: D000927:Anticonvulsants / Q000627:therapeutic use; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004827:Epilepsy / Q000453:epidemiology; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016011:Normal Distribution; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012984:Software / Q000706:statistics & numerical data; D005680:gamma-Aminobutyric Acid / Q000031:analogs & derivatives / Q000627:therapeutic use
chemical_list: D000927:Anticonvulsants; C017985:progabide; D005680:gamma-Aminobutyric Acid