- Hardcover: 240 pages
- Publisher: Chapman and Hall/CRC; 1 edition (July 30 2002)
- Language: English
- ISBN-10: 1584883073
- ISBN-13: 978-1584883074
- Product Dimensions: 23.4 x 16.3 x 1 cm
- Shipping Weight: 454 g
- Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)
- Amazon Bestsellers Rank: #369,920 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Description
Product Description
Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with longitudinal and clustered data. Developed specifically to accommodate these data types, the method of Generalized Estimating Equations (GEE) extends the GLM algorithm to accommodate the correlated data encountered in health research, social science, biology, and other related fields.
Generalized Estimating Equations provides the first complete treatment of GEE methodology in all of its variations. After introducing the subject and reviewing GLM, the authors examine the different varieties of generalized estimating equations and compare them with other methods, such as fixed and random effects models. The treatment then moves to residual analysis and goodness of fit, demonstrating many of the graphical and statistical techniques applicable to GEE analysis.
With its careful balance of origins, applications, relationships, and interpretation, this book offers a unique opportunity to gain a full understanding of GEE methods, from their foundations to their implementation. While equally valuable to theorists, it includes the mathematical and algorithmic detail researchers need to put GEE into practice.
Generalized Estimating Equations provides the first complete treatment of GEE methodology in all of its variations. After introducing the subject and reviewing GLM, the authors examine the different varieties of generalized estimating equations and compare them with other methods, such as fixed and random effects models. The treatment then moves to residual analysis and goodness of fit, demonstrating many of the graphical and statistical techniques applicable to GEE analysis.
With its careful balance of origins, applications, relationships, and interpretation, this book offers a unique opportunity to gain a full understanding of GEE methods, from their foundations to their implementation. While equally valuable to theorists, it includes the mathematical and algorithmic detail researchers need to put GEE into practice.
Inside This Book
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First Sentence
In this text we address the general field of panel data analysis including longitudinal data analysis, but our main focus is on those models generally classified as generalized estimating equations, or GEE. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
In this text we address the general field of panel data analysis including longitudinal data analysis, but our main focus is on those models generally classified as generalized estimating equations, or GEE. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most helpful customer reviews
5.0 out of 5 stars
GEE explained with all the computational details,
By
This review is from: Generalized Estimating Equations (Hardcover)
GEE is an extension of the Generalized Linear Models that handles the correlation structure found in panel data and longitudinal or repeated measures analysis. The idea goes back to a paper by Zeger and Liang in 1986 and is well cover in their 1994 book with Diggle and its most recent revision. However, over the years the statistical packages have implemented GEE and variations of it in many different ways. This important new book not only provides a detailed description of GEE for theoreticians and practitioners but it also presents comparisons of how the various software products implement it. The packages include GLIM, SAS STAT/GENMOD, S-PLUS, Stata and RTI's product SUDAAN (designed specifically for survey data).This book is well written and comprehensive and will make a great reference book. It even provides a chapter on model diagnostics. Many examples are illustrated using the various software packages.
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Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
5.0 out of 5 stars (2 customer reviews) 27 of 28 people found the following review helpful
5.0 out of 5 stars
great book on a topic important to biostatisticians and containing details about computation,
By Michael R. Chernick "statman31147" - Published on Amazon.com
This review is from: Generalized Estimating Equations (Hardcover)
GEE is an extension of the Generalized Linear Models that handles the correlation structure found in panel data and longitudinal or repeated measures analysis. The idea goes back to a paper by Zeger and Liang in 1986 and is well cover in their 1994 book with Diggle and its most recent revision. However, over the years the statistical packages have implemented GEE and variations of it in many different ways. This important new book not only provides a detailed description of GEE for theoreticians and practitioners but it also presents comparisons of how the various software products implement it. The packages include GLIM, SAS STAT/GENMOD, S-PLUS, Stata and RTI's product SUDAAN (designed specifically for survey data).This book is well written and comprehensive and will make a great reference book. It even provides a chapter on model diagnostics. Many examples are illustrated using the various software packages. As repeated measures or longitudinal data is very common in clinical trials this work is very imprtant and the text is a great reference for biostatisticians conducting clinical trials for pharmaceutical or medical device companies and for those in the medical research field that do such trials for research funding agencies. 4 of 5 people found the following review helpful
5.0 out of 5 stars
Excellent, clearly written,
By Russell Barbour "skip" - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Generalized Estimating Equations (Hardcover)
I am just starting to apply GEE and this book is a must for getting a clear conceptual grasp of the subject and practical hands on demonstrations of how to apply it.
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