Generalized estimating equations

Author(s)

• Hardin, James W. (James William)
• Hilbe, Joseph M.

Bibliographic Information

Generalized estimating equations

James W. Hardin, Joseph M. Hilbe

Chapman & Hall/CRC, c2003

Note

Includes bibliographical references (p. 215-218) and index

Description and Table of Contents

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.

Table of Contents

INTRODUCTION Notational Conventions A Short Review of Generalized Linear Models Software Exercises MODEL CONSTRUCTION AND ESTIMATING EQUATIONS Independent Data Estimating the Variance of the Estimates Panel Data Estimation Summary Exercises GENERALIZED ESTIMATING EQUATIONS Population-Averaged (PA) and Subject-Specific (SS) Models The PA-GEE for GLMs The SS-GEE for GLMs The GEE2 for GLMs GEEs for Extensions of GLMs Further Developments and Applications Missing Data Choosing an Appropriate Model Summary Exercises RESIDUALS, DIAGNOSTICS, AND TESTING Criterion Measures Analysis of Residuals Deletion Diagnostics Goodness of Fit (Population-Averaged Models) Testing Coefficients in the PA-GEE Model Assessing the MCAR Assumption of PA-GEE Models Summary Exercises PROGRAMS AND DATASETS Programs Datasets References Author Index Subject Index

by "Nielsen BookData"

Details

• NCID: BA59981096
• ISBN:
  • 1584883073
• LCCN: 2002067404
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Boca Raton ; Washington, D.C.
• Pages/Volumes: xiii, 222 p.
• Size: 24 cm
• Subject Headings:
  • LCSH : Generalized estimating equations