Analysis of Longitudinal Data

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

Overview

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.

Product Details

ISBN-13:	9780191664335
Publisher:	OUP Oxford
Publication date:	03/14/2013
Series:	Oxford Statistical Science Series , #25
Sold by:	Barnes & Noble
Format:	eBook
File size:	30 MB
Note:	This product may take a few minutes to download.

About the Author

Peter Diggle, Department of Mathematics and Statistics, University of Lancaster Patrick Heagerty, Biostatistics department University of Washington Kung-Yee Liang, Biostatistics department, Johns Hopkins University Scott Zeger, Biostatistics department, Johns Hopkins University

Table of Contents

1. Introduction2. Design considerations3. Exploring longitudinal data4. General linear models for longitudinal data5. Parametric models for covariance structure6. Analysis of variance methods7. Generalized linear models for longitudinal data8. Marginal models9. Random effects models10. Transition models11. Likelihood-based methods for categorical data12. Time-dependent covariates13. Missing values in longitudinal data14. Additional topicsAppendix Statistical backgroundBibliographyIndex

Editorial Reviews

"...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

From the Publisher