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 ...

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Analysis of Longitudinal Data

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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.

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Editorial Reviews

From the Publisher

"...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

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Product Details

  • ISBN-13: 9780191664335
  • Publisher: OUP Oxford
  • Publication date: 6/20/2002
  • Series: Oxford Statistical Science Series
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 2
  • Sales rank: 1,195,137
  • File size: 29 MB
  • Note: This product may take a few minutes to download.

Meet 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

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Table of Contents

1 Introduction 1
2 Design considerations 22
3 Exploring longitudinal data 33
4 General linear models for longitudinal data 54
5 Parametric models for covariance structure 81
6 Analysis of variance methods 114
7 Generalized linear models for longitudinal data 126
8 Marginal models 141
9 Random effects models 169
10 Transition models 190
11 Likelihood-based methods for categorical data 208
12 Time-dependent covariates 245
13 Missing values in longitudinal data 282
14 Additional topics 319
App.: Statistical background 337
Bibliography 349
Index 369
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