Quasi-Least Squares Regression
By Justine Shults, Joseph M. Hilbe
To Be Published December 15th 2013 by Chapman and Hall/CRC – 224 pages
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Description
Drawing on the authors’ substantial expertise in modeling longitudinal and clustered data, this book presents a comprehensive treatment of quasi-least squares (QLS) regression—a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present an overview and detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitudinal data, familial data, and data with multiple sources of correlation. In some settings, QLS also allows for improved analysis with an unstructured correlation matrix.
Special focus is given to goodness-of-fit analysis as well as new strategies for selecting the appropriate working correlation structure for QLS and GEE. A chapter on longitudinal binary data tackles recent issues raised in the statistical literature regarding the appropriateness of semi-parametric methods, such as GEE and QLS, for the analysis of binary data; this chapter includes a comparison with the first-order Markov maximum-likelihood (MARK1ML) approach for binary data.
Examples throughout the book demonstrate each topic of discussion. In particular, a fully worked out example leads readers from the planning stages of a study (including sample size considerations) through model construction and interpretation. Code is provided so that readers can replicate many of the examples in Stata, often with corresponding R, SAS, or MATLAB® code offered in the text or on the book’s website.
Contents
Overview of GEE and QLS. Review of Generalized Linear Models. History and Theory of QLS Regression. Correlation Structures for Analysis of Clustered and Longitudinal Data. Analysis of Multi-Level Correlated Data. Analysis of Familial Data. Additional Considerations: Data That Stabilize over Time and That Have Non-Constant Variance. Analysis of Correlated Binary Data. Sample Size Calculation. Selection of a Working Correlation Structure and Assessment of Goodness of Fit. A Worked Example from the Planning Stages of a Study through Its Analysis and Assessment of Model Assumptions. Discussion and Demonstration of Comparisons with Alternative Approaches. Appendices.
Author Bio
Justine Shults is an associate professor and co-director of the Pediatrics Section in the Division of Biostatistics in the Perelman School of Medicine at the University of Pennsylvania, where she is the principal investigator of the biostatistics training grant in renal and urologic diseases. She is the Statistical Editor of the Journal of the Pediatric Infectious Disease Society and the Statistical Section Editor of Springer Plus. Professor Shults (with N. Rao Chaganty) developed Quasi-Least Squares (QLS) and was funded by the National Science Foundation and the National Institutes of Health to extend QLSand develop user-friendly software for implementing her new methodology. She has authored or co-authored over 100 peer-reviewed publications, including the initial papers on QLS for unbalanced and unequally spaced longitudinal data and on MARK1ML and the choice of working correlation structure for GEE.
Joseph M. Hilbe is a Solar System Ambassador with the Jet Propulsion Laboratory, an adjunct professor of statistics at Arizona State University, and an Emeritus Professor at the University of Hawaii. An elected fellow of the American Statistical Association and an elected member of the International Statistical Institute (ISI), Professor Hilbe is president of the International Astrostatistics Association as well as chair of the ISI Sports Statistics and Astrostatistics committees. He has authored two editions of the bestseller Negative Binomial Regression, Logistic Regression Models, and Astrostatistical Challenges for the New Astronomy. He also co-authored Methods of Statistical Model Estimation (with A. Robinson), Generalized Estimating Equations, Second Edition (with J. Hardin), and R for Stata Users (with R. Muenchen), as well as 17 encyclopedia articles and book chapters in the past five years.
Name: Quasi-Least Squares Regression (Hardback) – Chapman and Hall/CRC
Description: By Justine Shults, Joseph M. Hilbe. Drawing on the authors’ substantial expertise in modeling longitudinal and clustered data, this book presents a comprehensive treatment of quasi-least squares (QLS) regression—a computational approach for the estimation of correlation...
Categories: Statistics & Computing, Statistics for the Biological Sciences, Statistical Theory & Methods