Analysis of Longitudinal Data by Peter DiggleAnalysis of Longitudinal Data by Peter Diggle

Analysis of Longitudinal Data

byPeter Diggle, Patrick Heagerty, Kung-Yee Liang

Paperback | April 14, 2013

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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 andbiomedical 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 parallelwith 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 fortime-dependent predictors.
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.
Title:Analysis of Longitudinal DataFormat:PaperbackDimensions:400 pages, 9.21 × 6.14 × 0.8 inPublished:April 14, 2013Publisher:OUPLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199676755

ISBN - 13:9780199676750


Table of Contents

1. Introduction2. Design considerations3. Exploring longitudinal data4. General linear models5. 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 topicsAppendixBibliographyIndex

Editorial Reviews

"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