Applied Longitudinal Analysis / Wiley Series in Probability and Statistics (ePub)

Praise for the First Edition

". . . [this book] should be on the shelf of everyone interested
in . . . longitudinal data analysis."

--Journal of the American Statistical Association

Features newly developed topics and applications of the
analysis of longitudinal data

Applied Longitudinal Analysis, Second Edition presents
modern methods for analyzing data from longitudinal studies and now
features the latest state-of-the-art techniques. The book
emphasizes practical, rather than theoretical, aspects of methods
for the analysis of diverse types of longitudinal data that can be
applied across various fields of study, from the health and medical
sciences to the social and behavioral sciences.

The authors incorporate their extensive academic and research
experience along with various updates that have been made in
response to reader feedback. The Second Edition features six newly
added chapters that explore topics currently evolving in the field,
including:

* Fixed effects and mixed effects models

* Marginal models and generalized estimating equations

* Approximate methods for generalized linear mixed effects
models

* Multiple imputation and inverse probability weighted
methods

* Smoothing methods for longitudinal data

* Sample size and power

Each chapter presents methods in the setting of applications to
data sets drawn from the health sciences. New problem sets have
been added to many chapters, and a related website features sample
programs and computer output using SAS, Stata, and R, as well as
data sets and supplemental slides to facilitate a complete
understanding of the material.

With its strong emphasis on multidisciplinary applications and
the interpretation of results, Applied Longitudinal
Analysis, Second Edition is an excellent book for courses on
statistics in the health and medical sciences at the
upper-undergraduate and graduate levels. The book also serves as a
valuable reference for researchers and professionals in the
medical, public health, and pharmaceutical fields as well as those
in social and behavioral sciences who would like to learn more
about analyzing longitudinal data.