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Print Price: $69.50

Format:
Paperback
400 pp.
156 mm x 234 mm

ISBN-13:
9780199676750

Publication date:
April 2013

Imprint: OUP UK

Analysis of Longitudinal Data

Second Edition

Peter Diggle, Patrick Heagerty, Kung-Yee Liang and Scott Zeger

Series : Oxford Statistical Science Series

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix.

This second edition, published for the first time in paperback, provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

Readership : Suitable for graduate students and researchers in probability and statistics, professionals in biostatistics.

Reviews

"The book is readable, well-written, and amply illustrated"

--Technometrics, August 1995 (previous edition)
"It belongs in the possession of every statistician who encouters longitudinal data."

--Journal of the American Statistical Association
". . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research."

--Statistics in Medicine
"The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful."

--Pharmaceutical Statistics
"The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated."

--Pharmaceutical Statistics
"Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data."

--Zentralblatt MATH

1. Introduction
2. Design considerations
3. Exploring longitudinal data
4. General linear models
5. Parametric models for covariance structure
6. Analysis of variance methods
7. Generalized linear models for longitudinal data
8. Marginal models
9. Random effects models
10. Transition models
11. Likelihood-based methods for categorical data
12. Time-dependent covariates
13. Missing values in longitudinal data
14. Additional topics
Appendix
Bibliography
Index

There are no Instructor/Student Resources available at this time.

Peter Diggle is in the Department of Mathematics and Statistics at the University of Lancaster. Patrick Heagerty is in the Biostatistics Department at the University of Washington. Kung-Yee Liang is in the Biostatistics Department at Johns Hopkins University. Scott Zeger is in the Biostatistics department at Johns Hopkins University.

Making Sense - Margot Northey and Joan McKibbin

Special Features

A thorough and expanded version of a classic text.
Important reference for professional statisticians as well as for graduate students.