Analysis of Longitudinal Data
The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated
measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous
data, and models and methods for handling data and missing values.
already the classic book on longitudinal data analysis, July 24, 2000
When this book came out in 1994 there was a great need to look differently at clinical data on subjects. Typically such data would have repeated measurements over time for many subjects but for only a few time points (say three to five). Standard analysis of variance methods do not properly account for within patient correlation between measurements. Time series analysis generally is good for treating long series (but usually only one or a few). In the clinical setting we often are considering hundreds of patients over short time intervals. This book is clearly written for intermediate level statistics students.
The field is important and rapidly developing. Though slightly dated the book is still an excellent introduction to the subject and a very good reference. However, a second edition is in the works and should be out in about one year. I recently took a short course from the authors and I know that the second edition will have some nice features including the latest advances for dealing with missing data and ways to combined the information from time to event data with the repeated measures data. It may be that if longitudinal data analysis is important to you, read the first edition at your favorite university library and save your money for the second edition.
The book includes some nice treatment of the important but often neglected topic of sample size determination.