This textbook by Peter Diggle covers advanced models for the analysis of longitudinal data. It focuses on statistical methods and applications, with a strong emphasis on practical examples from the agricultural and biomedical sciences. This second edition includes extensive revisions and two new chapters on models for discrete repeated measurements and statistical models for time-dependent predictors.
Overview
Analysis for longitudinal data describes both the underlying theory and the application of models for longitudinal datasets. The text covers design issues, exploratory analysis methods, linear models for continuous data, generalized linear models for discrete data, and techniques for handling missing values. Each topic is illustrated with detailed examples, ensuring the reproducibility of results with the accompanying datasets. This edition provides a comprehensive and up-to-date professional reference for researchers and professionals in biostatistics.
Product specifications
- Author: Peter Diggle (Department of Mathematics and Statistics, University of Lancaster)
- Series: Oxford Statistical Science Series, Vol. 25
- Publisher: Oxford University Press
- Publication date: 2013-03-14
- Number of pages: 400
- ISBN: 9780199676750
- Subject: Probability and statistics
- BISAC: MATHEMATICS / Probability & Statistics / General
About the author
Peter Diggle, Department of Mathematics and Statistics, University of Lancaster
Patrick Heagerty, Department of Biostatistics, University of Washington
Kung-Yee Liang, Department of Biostatistics, Johns Hopkins University
Scott Zeger, Department of Biostatistics, Johns Hopkins University