Concise Handbook of Experimental Methods for the Behavioral and Biological Sciences
Chapter 6 covers the very important linear mixed model approach to the analysis of normally distributed repeated measurements. The chapter includes a nice discussion of the effects of choice of covariance structure on estimates and tests. My only criticism of this material is that only 41 pages are devoted to what is arguably the most important area of modern analysis of repeated measurements where the outcome variables are approximately normally distributed at each time point. Perhaps a little more detail could have been given here, with less material presented in Chapters 2–4. In contrast, 75 pages are devoted to weighted least squares (WLS) analysis of repeated categorical outcomes and randomization model methods for one-sample repeated measurements. (The latter methods can be used when the sample size is too small, the number of time points is too large, and/or the response variables have too many possible values for the WLS approach to be appropriate.) Having said that, the information in Chapters 7 and 8 on WLS and randomization model methods is very useful and may set this book apart from other textbooks in terms of completeness. Also, Chapter 9 gives considerable detail (59 pages) on the very important methods based on extensions of generalized linear models (including the GEE method). Chapter 10 is devoted to current nonparametric methods. Although the author has chosen not to include programming statements or computer output in the book (which I believe is a wise decision), it would be helpful for students to have some examples of statistical programming on a website that could be updated appropriately as the software changes. Most other books on repeated measurements (a comprehensive list of books on repeated measurements is given on page 10) tend to focus on specialized topics. In my opinion, Statistical Methods for the Analysis of Repeated Measurements book is the most comprehensive and readable of the lot. I would highly recommend its use as a text for a semester-length graduate course for biostatistics and statistics students and also as a resource book for consulting biostatisticians and statisticians. In addition, this book would be a valuable resource for students from other  elds of study (e.g., the health sciences) who have a statistical aptitude. The book is de nitely worth the price.