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Applied Longitudinal Analysis (Wiley Series in Probability and Statistics), by Garrett M. Fitzmaurice, Nan M. Laird, James H. Ware
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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.
- Sales Rank: #139267 in eBooks
- Published on: 2012-10-23
- Released on: 2012-10-23
- Format: Kindle eBook
Review
“The text is well-organized and clearly written. It is accessible to researchers with varying levels of statistical expertise, with plenty of data examples that make reading and learning enjoyable. I recommend it to biostatisticians as well as to clinicians and other health researchers who may not have much statistical training . . . Applied Longitudinal Analysisis generally my first recommendation when asked for a valuable resource in the field due to the breadth of topics covered and its practical utility.”� (Journal of Biopharmaceutical Statistics, 1 January 2013)
“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 analysing longitudinal data.” (Zentralblatt MATH, 2012)
"This book provides very broad coverage of modern methods for longitudinal data analysis from an applied perspective ... I highly recommend this book to statisticians and quantitative researchers who encounter longitudinal and/or clustered data. In addition, I think the book would be an excellent choice as the primary textbook in an applied longitudinal data course." (Journal of Biopharmaceutical Statistics, 2013)�
From the Back Cover
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.
About the Author
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.
Most helpful customer reviews
36 of 38 people found the following review helpful.
great addition to the literture on longitudinal data analysis
By Michael R. Chernick
While the text by Diggle, Heagerty, Liang and Zeger now in its second edition is the first and in my opinion still the best book to cover the theory and methods of longitudinal data analysis, the subject has such great importance in biostatistics and clinical trial research that a number of excellent competitors have now come. This text is certainly one.
Nan Laird and James Ware are Harvard Professors of Biostatistics with a great deal of experience studying and publishing research on longitudinal data. Along with Fitzmaurice they have put together a book that provides a strong foundation in the methodology and a wealth of applications based on their experience.
30 of 32 people found the following review helpful.
Perfect for the applied researcher
By Peter Flom
If you need to do longitudinal analyses, and have a moderate mathermatical background, this is a book you should get, particularly if you use SAS. The authors present a wide variety of models clearly, describe their advantages and disadvantages, and illustrate how to use SAS to fit them. They keep the technical level modest (a little use of matrix algebra, but no calculus; not in theorem-proof style) while not sacrificing needed detail. In addition, they provide, at the end of each chapter, two sets of references: One at a similar level to this book, and one with more advanced material for those who wish (and are able) to explore it.
14 of 16 people found the following review helpful.
My favorite introductory statistics book
By Gus Harlow
The authors have done a masterful job. They've created a book that is accessable to those without a strong mathematics background, but still interesting to those with such a background. The scope is broad, yet one does not feel "shortchanged" on any topic covered. They cover both linear and generalized linear models, with and without mixed effects. Part IV contains what the authors call advanced topics such as missing data and multilevel models and their lucidity, given such brief treatment is astonishing.
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