Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling (eBook)

This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion parameters. The new estimating equations in these two cases are generated by maximizing a 'likelihood' function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessian matrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models.

George J. Knafl is Biostatistician and Professor Emeritus in the School of Nursing of the University of North Carolina at Chapel Hill where he taught statistics courses for doctoral nursing students, consulted with doctoral students and faculty on their research, and conducted his own research. He has over 45 years of experience in teaching, consulting, and research in statistics. He has continued to conduct research involving development of methods for searching through alternative models for different types of statistical data and application of those methods to the analysis of a variety of health science data sets. He is also Professor Emeritus in the College of Computing and Digital Media at DePaul University and has served on the faculties of the Schools of Nursing at Yale University and at the Oregon Health and Science University.