Generalized Estimating Equations, Second Edition

ISBN-10: 1439881138
ISBN-13: 9781439881132
Edition: 2nd 2013 (Revised)
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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  More...

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Book details

List price: $62.99
Edition: 2nd
Copyright year: 2013
Publisher: Taylor & Francis Group
Publication date: 12/10/2012
Binding: Hardcover
Pages: 277
Size: 6.50" wide x 9.50" long x 1.00" tall
Weight: 1.518

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.

Joseph M. Hilbe is an emeritus professor at the University of Hawaii, an adjunct professor of statistics at Arizona State University, and a Solar System Ambassador with NASA/Jet Propulsion Laboratory, Caltech. An elected Fellow of the American Statistical Association and elected member of the International Statistical Institute, Dr. Hilbe is currently President of the International Astrostatistics Association, is a full member of the American Astronomical Society, and Chairs the Statistics in Sports section of the American Statistical Association (ASA). He has authored fifteen books in statistical modeling, and over 200 book chapters, encyclopedia entries, journal articles, and published statistical software, and is currently on the editorial board of seven academic journals. During the 1990's Dr Hilbe was on the founding executive committee of the ASA Section on Health Policy Statistics, and served in various capacities in the health research industry, including: CEO of National Health Economics and Research Corp.; Director of Research at Transitional Hospitals Corp, a national chain of long term hospitals; Senior Statistician of NRMI-2, Genentech's National Registry for Myocardial Infarctions; lead biostatistical consultant, Hoffman-La Roche's National Canadian Registry for Cardiovascular Disease; and was Senior Statistical Consultant for HCFA's Medicare Infrastructure Project.

Preface
Introduction
Notational conventions and acronyms
A short review of generalized linear models
A brief history of GLMs
GLMs as likelihood-based models
GLMs and correlated data
GLMs and overdispersed data
Scaling standard errors
The modified sandwich variance estimator
The basics of GLMs
Link and variance functions
Algorithms
Software
R
SAS
Stata
SUDAAN
Exercises
Model Construction and Estimating Equations
Independent data
Optimization
The FLML estimating equation for linear regression
The FLML estimating equation for Poisson regression
The FLML estimating equation for Bernoulli regression
The LLML estimating equation for GLMs
The LLMQL estimating equation for GLMs
Estimating the variance of the estimates
Model-based variance
Empirical variance
Pooled variance
Panel data
Pooled estimators
Fixed-effects and random-effects models
Unconditional fixed-effects models
Conditional fixed-effects models
Random-effects models
Population-averaged and subject-specific models
Estimation
Summary
Exercises
R code for selected output
Generalized Estimating Equations
Population-averaged (PA) and subject-specific (SS) models
The PA-GEE for GLMs
Parameterizing the working correlation matrix
Exchangeable correlation
Autoregressive correlation
Stationary correlation
Nonstationary correlation
Unstructured correlation
Fixed correlation
Free specification
Estimating the scale variance (dispersion parameter)
Independence models
Exchangeable models
Estimating the PA-GEE model
The robust variance estimate
A historical footnote
Convergence of the estimation routine
ALR: Estimating correlations for binomial models
Quasi-least squares
Summary
The SS-GEE for GLMs
Single random-effects
Multiple random-effects
Applications of the SS-GEE
Estimating the SS-GEE model
Summary
The GEE2 for GLMs
GEEs for extensions of GLMs
Multinomial logistic GEE regression
Proportional odds GEE regression
Penalized GEE models
Cox proportional hazards GEE models
Further developments and applications
The PA-GEE for GLMs with measurement error
The PA-EGEE for GLMs
The PA-REGEE for GLMs
Quadratic inference function for marginal GLMs
Missing data
Choosing an appropriate model
Marginal effects
Marginal effects at the means
Average marginal effects
Summary
Exercises
R code for selected output
Residuals, Diagnostics, and. Testing
Criterion measures
Choosing the best correlation structure
Alternatives to the original QIC
Choosing the best subset of covariates
Analysis of residuals
A nonparametric test of the randomness of residuals
Graphical assessment
Quasivariance functions for PA-GEE models
Deletion diagnostics
Influence measures
Leverage measures
Goodness of fit (population-averaged models)
Proportional reduction in variation
Concordance correlation
A X<sup>2</sup> goodness of fit test for PA-GEE binomial models
Testing coefficients in the PA-GEE model
Likelihood ratio tests
Wald tests
Score tests
Assessing the MCAR assumption of PA-GEE models
Summary
Exercises
Programs and Datasets
Programs
Fitting PA-GEE models in Stata
Fitting PA-GEE models in SAS
Fitting PA-GEE models in R
Fitting ALR models in SAS
Fitting PA-GEE models in SUDAAN
Calculating QIC(P) in Stata
Calculating QIC(HH) in Stata
Calculating QICu in Stata
Graphing the residual runs test in R
Using the fixed correlation structure in Stata
Fitting quasi/variance PA-GEE models in R
Fitting GLMs in R
Fitting FE models in R using the GAMLSS package
Fitting RE models in R using the LME4 package
Datasets
Wheeze data
Ship accident data
Progabide data
Simulated logistic data
Simulated user-specified correlated data
Simulated measurement error data for the PA-GEE
References
Author index
Subject index

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