- Series: Oxford Statistical Science Series (Book 25)
- Hardcover: 396 pages
- Publisher: Oxford University Press; 2 edition (August 15, 2002)
- Language: English
- ISBN-10: 0198524846
- ISBN-13: 978-0198524847
- Product Dimensions: 9.3 x 1.1 x 6.4 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 3.5 out of 5 stars See all reviews (4 customer reviews)
- Amazon Best Sellers Rank: #1,351,243 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Analysis of Longitudinal Data 2nd Edition
Use the Amazon App to scan ISBNs and compare prices.
Featured Springer resources in biomedicine
Explore these featured titles in biomedicine. Learn more
Frequently bought together
Customers who bought this item also bought
"...it is well written, with wide coverage of biological and medical applications. It should continue to have a prominent place in libraries, and researchers who are interested in longitudinal data analysis will want a personal copy." --Journal of the American Statistical Association
About the Author
Peter Diggle is in the Department of Mathematics and Statistics, University of Lancaster. Patrick Heagerty is in the Biostatistics Department, University of Washington. Kung-Yee Liang and Scott Zeger are both in the Biostatistics Department, Johns Hopkins University.
Browse award-winning titles. See more
If you are a seller for this product, would you like to suggest updates through seller support?
Top Customer Reviews
So this type of analysis is similar to time series analysis. The difference is that time series are usually studied in the situation where a single series is observed for a long time and the analyst wants to determine future behavior based on an model constructed to fit this one observed series very well. The model is intended in the time series setting to describe a stochastic process (usually a stationary process or one transformed to stationarity by removal of trends). On the other hand in longitudinal analysis each patients profile over time is usually a very short series and the collection of these series over several patients in a particular treatment group are view to come from the same stochastic process. So the data represent several short partial realizations of the stochastic process while a time series is a long, single partial realization.
Since the data differ the methods of analyses differ also. For time seies analysis the autoregressive integrated moving average models of Box and Jenkins are often employed while for longitudinal data the mixed effect linear models are often the class of models chosen. The common theme is the structure of the covariance matrix for the observations in time series and the model noise terms in the case of the linear mixed models.
Zeger and Liang were among the leaders in developing successful modelling for these data. In a series of articles they develop a restricted maximum likelihood approach to the problem of estimating the model parameters and introduce a method called GEE an acronym for generalized estimating equations. The first edition of this book was very popular in the statistical community, particularly for statisticians working in the pharmaceutical industry. Along with Peter Diggle these three authors presented in the first edition this research organized into a single book for the first time. Now there is a plethora of books some prinarily theoretical and others primarily applied. The issue of missing data is very common to this type of data particularly when the data come from a clinical trial. The research of Molenberghs and Verbeke, covered by them in some repeated measures books, has shown these models to be among the most useful for handling missing data in realistic ways.
This second edition of this book has even greater coverage of topics and includes a fourth author Patrick Heagerty. Each of the four authors are skill research statisticians who specialize in biostatistics and particularly longitudinal data. While today there are many books to choose, this text continues ot be among the best.