Generalized Estimating Equations
Authors:
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James W. Hardin and Joseph M. Hilbe |
| Publisher: |
Chapman & Hall/CRC |
| Copyright: |
2003 |
| ISBN-13: |
978-1-58488-307-4 |
| Pages: |
222; hardcover |
| Price: |
$87.50 |
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Comment from the Stata technical group
The method of generalized linear models (GLM) is an integral part of the
data analyst’s toolkit, as it encompasses many models under one roof:
logistic and probit regressions, ordinary least squares, ordinal outcome
regression, and regression models for the analysis of survival data, to name
a few. Nominal GLM, however, is inadequate when the data are longitudinal
or are otherwise grouped so that observations within the same group are
expected to be correlated. The method of generalized estimating equations
(GEE) is a generalization of GLM that takes into account this within-group
correlation.
This text is the sequel to the 2001 text,
Generalized Linear Models and
Extensions, by the same authors, and provides the first complete
treatment of GEE methodology. As with the previous text on GLM, this text
is filled with examples on using this methodology with Stata. In fact, the
principal author, James Hardin, developed much of the Stata software for
fitting GEE models while he was a senior statistician at StataCorp.
This text is heavy in mathematical and computational detail, but the
mathematics is balanced by an array of real-world datasets and analyses.
Thus the text should appeal to a wide audience, from the mathematical
statistician wishing to glean the current state of the GEE literature to the
professional researcher needing to fit a GEE model to solve a particular
problem.
Table of contents
1 Introduction
1.1 Notational conventions
1.2 A short review of generalized linear models
1.2.1 Historical review
1.2.2 Basics
1.2.3 Link and variance functions
1.2.4 Algorithms
1.3 Software
1.3.1 S-PLUS
1.3.2 SAS
1.3.3 Stata
1.3.4 SUDAAN
1.4 Exercises
2 Model Construction and Estimating Equations
2.1 Independent data
2.1.1 The FIML estimating equation for linear regression
2.1.2 The FIML estimating equation for Poisson regression
2.1.3 The FIML estimating equation for Bernoulli regression
2.1.4 The LIML estimating equation for GLMs
2.1.5 The LIMQL estimating equation for GLMs
2.2 Estimating the variance of the estimates
2.3 Panel data
2.3.1 Pooled estimators
2.3.2 Fixed-effects and random-effects models
2.3.2.1 Unconditional fixed-effects models
2.3.2.2 Conditional fixed-effects models
2.3.2.3 Random-effects models
2.3.3 Population-averages and subject-specific models
2.4 Estimation
2.5 Summary
2.6 Exercises
3 Generalized Estimating Equations
3.1 Population-averaged (PA) and subject-specific (SS) models
3.2 The PA-GEE for GLMs
3.2.1 Parameterizing the working correlation matrix
3.2.1.1 Exchangeable correlation
3.2.1.2 Autoregressive correlation
3.2.1.3 Stationary correlation
3.2.1.4 Nonstationary correlation
3.2.1.5 Unstructured correlation
3.2.1.6 Fixed correlation
3.2.1.7 Free specification
3.2.2 Estimating the scale variance (dispersion parameter)
3.2.2.1 Independence models
3.2.2.2 Exchangeable models
3.2.3 Estimating the PA-GEE model
3.2.4 Convergence of the estimation routine
3.2.5 ALR: Estimating correlations for binomial models
3.2.6 Summary
3.3 The SS-GEE for GLMs
3.3.1 Single random-effects
3.3.2 Multiple random-effects
3.3.3 Applications of the SS-GEE
3.3.4 Estimating the SS-GEE model
3.3.5 Summary
3.4 The GEE2 for GLMs
3.5 GEEs for extensions of GLMs
3.5.1 Generalized logistic regression
3.5.2 Cumulative logistic regression
3.6 Further developments and applications
3.6.1 The PA-GEE for GLMs with measurement error
3.6.2 The PA-EGEE for GLMs
3.6.3 The PA-REGEE for GLMs
3.7 Missing data
3.8 Choosing an appropriate model
3.9 Summary
3.10 Exercises
4 Residuals, Diagnostics, and Testing
4.1 Criterion measures
4.1.1 Choosing the best correlation structure
4.1.2 Choosing the best subset of covariates
4.2 Analysis of residuals
4.2.1 A nonparametric test of the randomness of residuals
4.2.2 Graphical assessment
4.2.3 Quasivariance functions for PA-GEE models
4.3 Deletion diagnostics
4.3.1 Influence measures
4.3.2 Leverage measures
4.4 Goodness of fit (population-averaged models)
4.4.1 Proportional reduction in variation
4.4.2 Concordance correlation
4.4.3 A x2 goodness of fit test for PA-GEE binomial models
4.5 Testing coefficients in the PA-GEE model
4.5.1 Likelihood ratio tests
4.5.2 Wald tests
4.5.3 Score tests
4.6 Assessing the MCAR assumption of PA-GEE models
4.7 Summary
4.8 Exercises
5 Programs and Datasets
5.1 Programs
5.1.1 Fitting PA-GEE models in Stata
5.1.2 Fitting PA-GEE models in SAS
5.1.3 Fitting PA-GEE models in S-PLUS
5.1.4 Fitting ALR models in SAS
5.1.5 Fitting PA-GEE models in SUDAAN
5.1.6 Calculating QIC in Stata
5.1.7 Calculating QICu in Stata
5.1.8 Graphing the residual runs test in S-PLUS
5.1.9 Using the fixed correlation structure in Stata
5.1.10 Fitting quasivariance PA-GEE models in S-PLUS
5.2 Datasets
5.2.1 Wheeze data
5.2.2 Ship accident data
5.2.3 Progabide data
5.2.4 Simulated logistic data
5.2.5 Simulated user-specific correlated data
5.2.6 Simulated measurement error data for the PA-GEE
References
Author Index
Subject Index
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