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Generalized estimating equations

Author: James W Hardin; Joseph M Hilbe
Publisher: Boca Raton, FL : CRC Press, [2013]
Edition/Format:   Print book : English : Second editionView all editions and formats
Database:WorldCat
Summary:
"Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the
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Document Type: Book
All Authors / Contributors: James W Hardin; Joseph M Hilbe
ISBN: 9781439881132 1439881138
OCLC Number: 818464282
Description: xv, 261 pages : illustration ; 24 cm
Contents: Preface --
1. Introduction --
1.1. Notational conventions and acronyms --
1.2. A short review of generalized linear models --
1.2.1. A brief history of GLMs --
1.2.1.1. GLMs as likelihood-based models --
1.2.1.2. GLMs and correlated data --
1.2.2. GLMs and overdispersed data --
1.2.2.1. Scaling standard errors --
1.2.2.2. The modified sandwich variance estimator --
1.2.3. The basics of GLMs --
1.2.4. Link and variance functions --
1.2.5. Algorithms --
1.3. Software --
1.3.1. R --
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. Optimization --
2.1.2. The FLML estimating equation for linear regression --
2.1.3. The FLML estimating equation for Poisson regression --
2.1.4. The FLML estimating equation for Bernoulli regression --
2.1.5. The LLML estimating equation for GLMs --
2.1.6. The LLMQL estimating equation for GLMs --
2.2. Estimating the variance of the estimates --
2.2.1. Model-based variance --
2.2.2. Empirical variance --
2.2.3. Pooled variance --
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-averaged and subject-specific models --
2.4. Estimation --
2.5. Summary --
2.6. Exercises --
2.7. R code for selected output --
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. The robust variance estimate --
3.2.5. A historical footnote --
3.2.6. Convergence of the estimation routine --
3.2.7. ALR: Estimating correlations for binomial models --
3.2.8. Quasi-least squares --
3.2.9. 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. Multinomial logistic GEE regression --
3.5.2. Proportional odds GEE regression --
3.5.3. Penalized GEE models --
3.5.4. Cox proportional hazards GEE models --
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 --
3.6.4. Quadratic inference function for marginal GLMs --
3.7. Missing data --
3.8. Choosing an appropriate model --
3.9. Marginal effects --
3.9.1. Marginal effects at the means --
3.9.2. Average marginal effects --
3.10. Summary --
3.11. Exercises --
3.12. R code for selected output --
4. Residuals, Diagnostics, and. Testing --
4.1. Criterion measures --
4.1.1. Choosing the best correlation structure --
4.1.2. Alternatives to the original QIC --
4.1.3. 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 X<sup>2</sup> 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 --
4.1. 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 R --
5.1.4. Fitting ALR models in SAS --
5.1.5. Fitting PA-GEE models in SUDAAN --
5.1.6. Calculating QIC(P) in Stata --
5.1.7. Calculating QIC(HH) in Stata --
5.1.8. Calculating QICu in Stata --
5.1.9. Graphing the residual runs test in R --
5.1.10. Using the fixed correlation structure in Stata --
5.1.11. Fitting quasi/variance PA-GEE models in R --
5.1.12. Fitting GLMs in R --
5.1.13. Fitting FE models in R using the GAMLSS package --
5.1.14. Fitting RE models in R using the LME4 package --
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-specified correlated data --
5.2.6. Simulated measurement error data for the PA-GEE --
References --
Author index --
Subject index.
Responsibility: James W. Hardin university of South Carolina, USA, Joseph M. Hilbe, Jet Propulsion Laboratory, California Institute of Technology, USA and Arizona State University, USA.
More information:

Abstract:

"Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book's website. This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentation of hypothesis testing and diagnostics, covering competing hierarchical models; and a more detailed examination of previously discussed subjects. Along with doubling the number of end-of-chapter exercises, this edition expands discussion of various models associated with GEE, such as penalized GEE, cumulative and multinomial GEE, survey GEE, and quasi-least squares regression. It also offers a thoroughly new presentation of model selection procedures, including the introduction of an extension to the QIC measure that is applicable for choosing among working correlation structures. See Professor Hilbe discuss the book"--

"CHAPTER 1 Preface Second Edition We are pleased to offer this second edition to Generalized Estimating Equations. This edition benefits from comments and suggestions from various sources given to us during the past ten years since the first edition was published. As a consequence, we have enhanced the text with a number of additions, including more detailed discussions of previously presented topics, program code for examples in text, and examination of entirely new topics related to GEE and the estimation of clustered and longitudinal models. We have also expanded discussion of various models associated with GEE; penalized GEE, survey GEE, and quasi-least squares regression, as well as the number of exercises given at the end of each chapter. We have also added material on hypothesis testing and diagnostics, including discussion of competing hierarchical models. We have also introduced more examples, and expanded the presentation of examples utilizing R software. The text has grown by 40 pages. This edition also introduces alternative models for ordered categorical outcomes and illustrates model selection approaches for choosing among various candidate specifications. We have expanded our coverage of model selection criterion measures and introduce an extension of the QIC measure which is applicable for choosing among working correlation structures (see 5.1.2 in particular). This is currently a subject of considerable interest among statisticians having an interest in GEE"--

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