Description
Book SynopsisDrawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regressiona computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a 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 tack
Trade Review
"The book does an excellent job of explaining basic concepts and techniques in the analysis of longitudinal and correlated data using QLS and GEE. Well-chosen data examples almost follow all the technical explanations, providing the readers a flavor on what problems QLS solves and how to solve those problems using software. Although the authors mainly use Stata to demonstrate the examples, they also provide web access to R, SAS, and MATLAB code and guidelines to replicate those examples, making the book appealing to a wide audience. The book also successfully incorporates some recent research work without raising its technical level. Therefore, the book will serve as a comprehensible guide to researchers who conduct analysis on correlated data. It would also be a good textbook for graduate students in statistics or biostatistics. Finally, I believe it would be a popular desk reference for methodology-oriented researchers who are interested in longitudinal studies and related fields."
—Journal of the American Statistical Association, March 2015
"This book deals with the quasi-least squares (QLS) regression, presenting a computational approach for the estimation of correlation parameters in the context of the generalized estimating equations (GEEs). … The book is provided with illustrative examples for each topic."
—Zentralblatt MATH 1306
Table of ContentsINTRODUCTION: Introduction. Review of Generalized Linear Models. QUASI-LEAST SQUARES THEORY AND APPLICATIONS: History and Theory of QLS Regression. Mixed Linear Structures and Familial Data. Correlation Structures for Clustered and Longitudinal Data. Analysis of Data with Multiple Sources of Correlation. Correlated Binary Data. Assessing Goodness of Fit and Choice of Correlation Structure for QLS and GEE. Sample Size and Demonstration. Bibliography. Index.
