Generalized estimating equations

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Correlated data are modeled using the same link function and linear predictor setup systematic component as in the case of independent responses. The random component is described by the same variance functions as in the independence case, but the covariance structure of the correlated responses must also be specified and modeled now! Independence - correlation between time points is independent. The quasi-likelihood estimators are estimates of quasi-likelihood equations which are called generalized estimating equations.

Recall, that we briefly discussed quasi-likelihood when we introduced overdispersion in Lesson 6. In general, there are no closed-form solutions, so the GEE estimates are obtained by using an iterative algorithm, that is iterative quasi-scoring procedure. GEE estimates of model parameters are valid even if the covariance is mis-specified because they depend on the first moment, e.

Generalized estimating equations | Stata

However, if the correlation structure is mis-specified, the standard errors are not good, and some adjustments based on the data empirical adjustment are needed to get more appropriate standard errors. Agresti points out that a chosen model in practice is never exactly correct, but choosing carefully a working correlation covariance structure can help with efficiency of the estimates.

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  • Fitting Generalized Estimating Equation (GEE) Regression Models in Stata.
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For more technical details see Agresti 4. Also, the end of this lecture has a bit more technical addendum based on Dr.

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Schafer's notes. The interpretation will depend on the chosen link function. Wald statistics based confidence intervals and hypothesis testing for parameters; recall they rely on asymptotic normality of estimator and their estimated covariance matrix. First we examine the method of "independence estimating equations," which incorrectly assumes that the observations within a subject are independent.

The sandwich estimator was first proposed by Huber and White ; Liang and Zeger applied it to longitudinal data. When within-subject correlations are not strong, Zeger suggests that the use of IEE with the sandwich estimator is highly efficient. Eberly College of Science. Printer-friendly version In Lesson 4 we introduced an idea of dependent samples, i.

Objective: Fit a model to repeated categorical responses, that is correlated and clustered responses, by GEE methodology. Variables: A response variable Y can be either continuous or categorical.

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Each y i can be, for example, a binomial or multinomial response. Systematic component: A linear predictor of any combination of continuous and discrete variables.

Generalized Estimating Equations

Covariance structure: Correlated data are modeled using the same link function and linear predictor setup systematic component as in the case of independent responses. Assumptions: The responses are Y 1 , Y 2 , Covariates can be the power terms or some other nonlinear transformations of the original independent variables, can have interaction terms. 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.

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.

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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. Toon meer Toon minder. 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. 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.

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. It can serve as supplemental reading in longitudinal data analysis classes as well.

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. 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.

click 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. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology. 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. Betrokkenen Auteur James W.