Mixed Effects Models for Complex Data

by Lang Wu

Overview

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors,

Product Details

ISBN-13:	9781040171042
Publisher:	CRC Press
Publication date:	11/11/2009
Sold by:	Barnes & Noble
Format:	eBook
Pages:	431
File size:	2 MB

About the Author

Lang Wu is an associate professor in the Department of Statistics at the University of British Columbia in Vancouver, Canada.

Table of Contents

Introduction. Mixed Effects Models. Missing Data, Measurement Errors, and Outliers. Mixed Effects Models with Missing Data. Mixed Effects Models with Covariate Measurement Errors. Mixed Effects Models with Censoring. Survival Mixed Effects (Frailty) Models. Joint Modeling Longitudinal and Survival Data. Robust Mixed Effects Models. Generalized Estimating Equations (GEEs). Bayesian Mixed Effects Models. Appendix. References. Index. Abstract.

What People are Saying About This

From the Publisher

This book could serve as a text for an advanced course at the Ph.D. level and as a reference to analysts who are familiar with basic statistical methodology for mixed effects models.
—Tena I. Katsaounis, Technometrics, November 2011

What I was most impressed by was the sheer breadth of complex models considered. Furthermore, unlike much of the research in the area, the book examines each of the complications, not merely in isolation, but in various combinations. … Considering the complexity of some of these models, the fact that the book does a good job of describing how to fit them in a clear manner is noteworthy. … The book is clear and lucidly written. It is set at an appropriate level for graduates and should be accessible to practitioners with at least some knowledge of mixed model methodology. It should also be of interest to researchers who might want to learn different modelling techniques.
—John T. Ormerod, Statistics in Medicine, 2011, 30

… as an introduction to what it says in the title of the book, the author has done an excellent job—the coverage is pretty comprehensive, detailed without too much mathematical technicality, and (most importantly) readable. I believe that it will become a useful reference in many libraries, personal and public.
—International Statistical Review (2010), 78, 3

Editorial Reviews

This book could serve as a text for an advanced course at the Ph.D. level and as a reference to analysts who are familiar with basic statistical methodology for mixed effects models.
—Tena I. Katsaounis, Technometrics, November 2011

What I was most impressed by was the sheer breadth of complex models considered. Furthermore, unlike much of the research in the area, the book examines each of the complications, not merely in isolation, but in various combinations. … Considering the complexity of some of these models, the fact that the book does a good job of describing how to fit them in a clear manner is noteworthy. … The book is clear and lucidly written. It is set at an appropriate level for graduates and should be accessible to practitioners with at least some knowledge of mixed model methodology. It should also be of interest to researchers who might want to learn different modelling techniques.
—John T. Ormerod, Statistics in Medicine, 2011, 30

… as an introduction to what it says in the title of the book, the author has done an excellent job—the coverage is pretty comprehensive, detailed without too much mathematical technicality, and (most importantly) readable. I believe that it will become a useful reference in many libraries, personal and public.
—International Statistical Review (2010), 78, 3

From the Publisher