Synopses & Reviews
This book provides a self-contained account of a wide range ofstatistical methods for the analysis of longitudinal data. Emphasizing the biomedical and agricultural sciences, the book covers each method's applicability and underlying statistical theory. Major topics include: design considerations, exploratory methods of analysis, linear models for continuous data, generalized linear models for discrete data, and models and methods for handling data with missing values. Worked examples are presented throughout and an appendix covers some basic statistical principles. This cogent and clear text will be welcomed by students across a wide range of the sciences.
Synopsis
The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous data, and models and methods for handling data and missing values.
Synopsis
Includes bibliographical references (p. [349]-368) and index.
Synopsis
The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated
measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous
data, and models and methods for handling data and missing values.
Table of Contents
1. Introduction
2. Design considerations
3. Exploring longitudinal data
4. General linear models for longitudinal data
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 Statistical background
Bibliography
Index