". . . [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:
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Fixed effects and mixed effects models
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Marginal models and generalized estimating equations
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Approximate methods for generalized linear mixed effects models
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Multiple imputation and inverse probability weighted methods
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Smoothing methods for longitudinal data
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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.
Garrett M. Fitzmaurice, ScD
, is Professor in the Department of Biostatistics at the Harvard School
of Public Health and Director of the Laboratory for Psychiatric
Biostatistics at McLean Hospital. A Fellow of the American Statistical
Association and advisor for the Wiley Series in Probability and
Statistics, Dr. Fitzmaurice's areas of research interest include
statistical methods for analyzing discrete longitudinal data and methods
for handling missing data.
Nan M. Laird, PhD, is Professor of Biostatistics at the Harvard
School of Public Health. A Fellow of the American Statistical
Association and Institute of Mathematical Sciences, she has published
extensively in the areas of statistical genetics, longitudinal
studies, missing or incomplete data, and analysis of multiple
informant data.
James H. Ware, PhD, is Frederick Mosteller Professor of
Biostatistics at the Harvard School of Public Health. A Fellow of the
American Statistical Association and statistical consultant to the New
England Journal of Medicine, he has made significant contributions to
the development of statistical methods for the design and analysis of
longitudinal studies.
