Generalized Estimating Equations

274 Pages·2012·2.627 MB·\274

by  James W. Hardin (Author), Joseph M. Hilbe (Author)

Second
Statistics
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Generalized Estimating Equations
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Generalized Estimating Equations, Second Edition updates the e
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best-selling previous edition, which has been the standard text on
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the subject since it was published a decade ago. Combining theory
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and application, the text provides readers with a comprehensive z
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discussion of GEE and related models. Numerous examples are
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employed throughout the text, along with the software code used to

create, run, and evaluate the models being examined. Stata is used E
as the primary software for running and displaying modeling output; s
associated R code is also given to allow R users to replicate Stata t
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examples. Specific examples of SAS usage are provided in the final m
chapter as well as on the book’s website.
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This second edition incorporates comments and suggestions from a
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variety of sources, including the Statistics.com course on longitudinal
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and panel models taught by the authors. Other enhancements include
Second Edition
an examination of GEE marginal effects; a more thorough presentation E
of hypothesis testing and diagnostics, covering competing hierarchical q
models; and a more detailed examination of previously discussed u
subjects. a
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Along with doubling the number of end-of-chapter exercises, this i
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edition expands discussion of various models associated with GEE,
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such as penalized GEE, cumulative and multinomial GEE, survey GEE, s
and quasi-least squares regression. It also offers a thoroughly new James W. Hardin
presentation of model selection procedures, including the introduction
of an extension to the QIC measure that is applicable for choosing
Joseph M. Hilbe
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Generalized
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Second Edition
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Generalized
Estimating
Equations
Second Edition
James W. Hardin
University of South Carolina, USA
Joseph M. Hilbe
Jet Propulsion Laboratory,
California Institute of Technology, USA
and
Arizona State University, USA
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CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2013 by Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business
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Version Date: 20121207
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To our wives, Mariaelena Castro-Hardin and Cheryl Lynn Hilbe,
and our children, Taylor Antonio Hardin, Conner Diego Hardin,
HeatherLynnHilbe,MichaelJosephHilbe,andMitchellJonHilbe.
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Contents
Preface xi
1 Introduction 1
1.1 Notational conventions and acronyms 2
1.2 A short review of generalized linear models 3
1.2.1 A brief history of GLMs 3
1.2.1.1 GLMs as likelihood-based models 4
1.2.1.2 GLMs and correlated data 4
1.2.2 GLMs and overdisperseddata 5
1.2.2.1 Scaling standard errors 6
1.2.2.2 The modified sandwich variance estimator 6
1.2.3 The basics of GLMs 7
1.2.4 Link and variance functions 9
1.2.5 Algorithms 10
1.3 Software 12
1.3.1 R 13
1.3.2 SAS 15
1.3.3 Stata 16
1.3.4 SUDAAN 16
1.4 Exercises 17
2 Model Construction and Estimating Equations 19
2.1 Independent data 19
2.1.1 Optimization 20
2.1.2 The FIML estimating equation for linear regression 20
2.1.3 The FIML estimating equation for Poissonregression 23
2.1.4 The FIML estimating equation for Bernoulli regression 24
2.1.5 The LIML estimating equation for GLMs 26
2.1.6 The LIMQL estimating equation for GLMs 29
2.2 Estimating the variance of the estimates 30
2.2.1 Model-based variance 30
2.2.2 Empirical variance 31
2.2.3 Pooled variance 33
2.3 Panel data 34
2.3.1 Pooled estimators 35
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viii CONTENTS
2.3.2 Fixed-effects and random-effects models 37
2.3.2.1 Unconditional fixed-effects models 37
2.3.2.2 Conditional fixed-effects models 39
2.3.2.3 Random-effects models 46
2.3.3 Population-averagedand subject-specific models 53
2.4 Estimation 54
2.5 Summary 55
2.6 Exercises 55
2.7 R code for selected output 57
3 Generalized Estimating Equations 59
3.1 Population-averaged(PA) and subject-specific (SS) models 59
3.2 The PA-GEE for GLMs 61
3.2.1 Parameterizing the working correlation matrix 62
3.2.1.1 Exchangeable correlation 63
3.2.1.2 Autoregressive correlation 71
3.2.1.3 Stationary correlation 74
3.2.1.4 Nonstationary correlation 76
3.2.1.5 Unstructured correlation 78
3.2.1.6 Fixed correlation 79
3.2.1.7 Free specification 79
3.2.2 Estimating the scale variance (dispersion parameter) 82
3.2.2.1 Independence models 83
3.2.2.2 Exchangeable models 88
3.2.3 Estimating the PA-GEE model 91
3.2.4 The robust variance estimate 92
3.2.5 A historical footnote 96
3.2.6 Convergence of the estimation routine 97
3.2.7 ALR: Estimating correlations for binomial models 97
3.2.8 Quasi-least squares 101
3.2.9 Summary 102
3.3 The SS-GEE for GLMs 104
3.3.1 Single random-effects 105
3.3.2 Multiple random-effects 107
3.3.3 Applications of the SS-GEE 108
3.3.4 Estimating the SS-GEE model 111
3.3.5 Summary 113
3.4 The GEE2 for GLMs 113
3.5 GEEs for extensions of GLMs 114
3.5.1 Multinomial logistic GEE regression 115
3.5.2 Proportionalodds GEE regression 116
3.5.3 Penalized GEE models 121
3.5.4 Cox proportional hazards GEE models 122
3.6 Further developments and applications 123
3.6.1 The PA-GEE for GLMs with measurement error 123
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CONTENTS ix
3.6.2 The PA-EGEE for GLMs 129
3.6.3 The PA-REGEE for GLMs 132
3.6.4 Quadratic inference function for marginal GLMs 135
3.7 Missing data 137
3.8 Choosing an appropriate model 146
3.9 Marginal effects 148
3.9.1 Marginal effects at the means 149
3.9.2 Average marginal effects 150
3.10 Summary 151
3.11 Exercises 153
3.12 R code for selected output 156
4 Residuals, Diagnostics, and Testing 161
4.1 Criterion measures 163
4.1.1 Choosing the best correlation structure 163
4.1.2 Alternatives to the original QIC 166
4.1.3 Choosing the best subset of covariates 169
4.2 Analysis of residuals 171
4.2.1 A nonparametric test of the randomness of residuals 171
4.2.2 Graphical assessment 172
4.2.3 Quasivariance functions for PA-GEE models 184
4.3 Deletion diagnostics 188
4.3.1 Influence measures 189
4.3.2 Leverage measures 194
4.4 Goodness of fit (population-averagedmodels) 195
4.4.1 Proportionalreduction in variation 195
4.4.2 Concordance correlation 195
4.4.3 A χ2 goodness of fit test for PA-GEE binomial models 197
4.5 Testing coefficients in the PA-GEE model 200
4.5.1 Likelihood ratio tests 201
4.5.2 Wald tests 203
4.5.3 Score tests 205
4.6 Assessing the MCAR assumption of PA-GEE models 206
4.7 Summary 209
4.8 Exercises 210
5 Programs and Datasets 213
5.1 Programs 213
5.1.1 Fitting PA-GEE models in Stata 214
5.1.2 Fitting PA-GEE models in SAS 215
5.1.3 Fitting PA-GEE models in R 216
5.1.4 Fitting ALR models in SAS 217
5.1.5 Fitting PA-GEE models in SUDAAN 218
5.1.6 Calculating QIC(P) in Stata 219
5.1.7 Calculating QIC(HH) in Stata 220
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