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(More customer reviews)Recently, I was asked to do some analysis on longitudinal patient data using fixed effects regression. This book provided an excellent, step-by-step approach on how to tackle the problem.
The first two chapters cover an overview of fixed effects and random effects modeling in the context of ordinary least squares. Then, Prof. Allison devotes a chapter to each of several topics: modeling binary outcomes, modeling counting outcomes, and modeling time to event (I admit that I stopped at Chapter 5).
I am particularly impressed by the way he shows different approaches to solving problems. And, that he compares the performance of different routines. It turns out that for many of my problems, proc genmod with GEE approximation is a good solution -- and one that finished in a few minutes rather than a few hours.
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Fixed Effects Regression Methods for Longitudinal Data Using SAS is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.
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