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## Generalized Estimating Equations, Second Edition

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$84.50 Print Add to cart What are VitalSource eBooks? Your access code will be emailed upon purchase. eBook not available for this title  Authors: James W. Hardin and Joseph M. Hilbe Publisher: Chapman & Hall/CRC Copyright: 2013 ISBN-13: 978-1-4398-8113-2 Pages: 277; hardcover Price:$84.50
 Authors: James W. Hardin and Joseph M. Hilbe Publisher: Chapman & Hall/CRC Copyright: 2013 ISBN-13: Pages: 277; eBook Price: \$0.00

Review of the first edition from the Stata Journal

### Comment from the Stata technical group

The method of generalized linear models (GLM) is an integral part of the data analyst’s toolkit, as it encompasses many models under one roof: logistic and probit regressions, ordinary least squares, ordinal outcome regression, and regression models for the analysis of survival data, to name a few. Nominal GLM, however, is inadequate when the data are longitudinal or are otherwise grouped so that observations within the same group are expected to be correlated. The method of generalized estimating equations (GEE) is a generalization of GLM that takes into account this within-group correlation.

This text is the sequel to the 2001 text, Generalized Linear Models and Extensions, by the same authors, and provides the first complete treatment of GEE methodology. As with the previous text on GLM, this text is filled with examples on using this methodology with Stata. In fact, the principal author, James Hardin, developed much of the Stata software for fitting GEE models while he was a senior statistician at StataCorp.

This text is heavy in mathematical and computational detail, but the mathematics is balanced by an array of real-world datasets and analyses. Thus the text should appeal to a wide audience, from the mathematical statistician wishing to glean the current state of the GEE literature to the professional researcher needing to fit a GEE model to solve a particular problem.

The second edition includes material about estimation of GEEs for survival analysis and robust variance estimates, as well as additional model-selection tools. Additional program code has also been included.