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## Details

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:

"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"--

## Reviews

*Editorial reviews*

Publisher Synopsis

"Overall, I found this to be a very useful book on GEE, and would recommend it to anyone planning to use GEE models in their data analysis. Both the theory and practical aspects of constructing and analysing such models is covered. Inclusion of code for many of the analyses is an excellent feature."-Ken J. Beath, Macquarie University, Australia, Australian and New Zealand Journal of Statistics, April 2017" ... the authors expand the text with several additions: (I) they examine and include entirely new topics related to GEE and the estimation of clustered and longitudinal models; (2) they add more detailed discussions of previously presented topics, including expanding the discussion of various models associated with GEE (penalized GEE, survey GEE, and quasi-least-square regression), adding material on hypothesis testing and diagnostics, and introducing alternative models for ordered categorical outcomes and an extension of the QIC, which is a model selection criterion measure; (3) they expand the amount of computer code by adding R code to duplicate the Stata examples wherever possible. In my opinion, the second edition is enhanced by the additions mentioned above, providing an excellent review of the GEE, wide coverage of its variations, and many useful computing techniques. I believe it would be a very useful reference book for practicing researchers and graduate students who are interested in research topics related to GEE."-CindyYu, Iowa State University in the Journal of the American Statistical Association, December 2013"The second edition ... adds a few new topics related to various extensions of GEE ... [and replaces] outdated S-PLUS codes with R scripts. Also, the number of exercises increased significantly ... . For those who want to use this book in the classroom, including me, having extra exercise sets is certainly a welcome addition. ... One main strength of this book is its comprehensive coverage of Stata implementation of the GEE. ... a valuable reference and is particularly useful for practitioners. It can serve as supplemental reading in longitudinal data analysis classes as well."-Woncheol Jang, Biometrics, September 2013Praise for the First Edition:"... well-written chapters ... . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. ... I enjoyed reading [this book] and recommend [it] very highly to the statistical community."-Journal of Statistical Computation and Simulation, February 2005"[The book] is comprehensive and covers much useful material with formulas presented in detail ... a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field."-Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004"Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM).The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing. Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology."-Journal of the American Statistics Association, March 2004"Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas."-Technometrics, 2003 Read more...

*User-contributed reviews*