Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data.
After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines.
Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.
Advances in Longitudinal Data Analysis: A Historical Perspective
Garrett Fitzmaurice and Geert Molenberghs
Parametric Modeling of Longitudinal Data
Parametric Modeling of Longitudinal Data: Introduction and Overview
Garrett Fitzmaurice and Geert Verbeke
Generalized Estimating Equations for Longitudinal Data Analysis
Stuart Lipsitz and Garrett Fitzmaurice
Generalized Linear Mixed-Effects Models
Sophia Rabe-Hesketh and Anders Skrondal
Nonlinear Mixed-Effects Models
Growth Mixture Modeling: Analysis with Non-Gaussian Random Effects
Bengt Muthén and Tihomir Asparouhov
Targets of Inference in Hierarchical Models for Longitudinal Data
Stephen W. Raudenbush
Nonparametric and Semiparametric Methods for Longitudinal Data
Nonparametric and Semiparametric Regression Methods: Introduction and Overview
Xihong Lin and Raymond J. Carroll
Nonparametric and Semiparametric Regression Methods for Longitudinal Data
Xihong Lin and Raymond J. Carroll
Functional Modeling of Longitudinal Data
Smoothing Spline Models for Longitudinal Data
Penalized Spline Models for Longitudinal Data
Babette A. Brumback, Lyndia C. Brumback, and Mary J. Lindstrom
Joint Models for Longitudinal Data
Joint Models for Longitudinal Data: Introduction and Overview
Geert Verbeke and Marie Davidian
Joint Models for Continuous and Discrete Longitudinal Data
Christel Faes, Helena Geys, and Paul Catalano
Random-Effects Models for Joint Analysis of Repeated-Measurement and Time-to-Event Outcomes
Peter Diggle, Robin Henderson, and Peter Philipson
Joint Models for High-Dimensional Longitudinal Data
Steffen Fieuws and Geert Verbeke
Incomplete Data: Introduction and Overview
Geert Molenberghs and Garrett Fitzmaurice
Selection and Pattern-Mixture Models
Paul S. Albert and Dean A. Follmann
Inverse Probability Weighted Methods
Michael G. Kenward and James R. Carpenter
Sensitivity Analysis for Incomplete Data
Geert Molenberghs, Geert Verbeke, and Michael G. Kenward
Estimation of the Causal Effects of Time-Varying Exposures
James M. Robins and Miguel A. Hernán
About the Editors
Garrett Fitzmaurice is Associate Professor of Psychiatry at the Harvard Medical School, Associate Professor of Biostatistics at the Harvard School of Public Health, and Foreign Adjunct Professor of Biostatistics at the Karolinska Institute in Sweden. He is a fellow of the American Statistical Association, a member of the International Statistical Institute, and a recipient of the American Statistical Association’s Excellence in Continuing Education Award.
Marie Davidian is William Neal Reynolds Distinguished Professor of Statistics at North Carolina State University and Adjunct Professor of Biostatistics and Bioinformatics at Duke University. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. Dr. Davidian is also a member of the International Statistical Institute and executive editor of Biometrics.
Geert Verbeke is Professor of Biostatistics in the Biostatistical Centre at the Catholic University of Leuven in Belgium. He is a past president of the Belgian Region of the International Biometric Society, joint editor of the Journal of the Royal Statistical Society, Series A, and an international representative on the board of directors and a fellow of the American Statistical Association. Jointly with Geert Molenberghs, Dr. Verbeke twice received the American Statistical Association’s Excellence in Continuing Education Award.
Geert Molenberghs is Professor of Biostatistics in the Center for Statistics at Hasselt University and in the Biostatistical Centre at the Catholic University of Leuven in Belgium. He is a fellow of the American Statistical Association, a member of the International Statistical Institute, a recipient of the Guy Medal in Bronze from the Royal Statistical Society, and coeditor of Biometrics. Together with Geert Verbeke, Dr. Molenberghs twice received the American Statistical Association’s Excellence in Continuing Education Award.
The scope is remarkable, and the degree of integration and polish is admirable. The contributors include many of the most innovative researchers in the field, and happily, many of the clearest writers as well. … a lively text with clear-eyed positions and well-argued recommendations on how to analyze data of specific structures. … [material] is all accessible, well explained, and well illustrated using examples … a very good book … an excellent resource for a graduate class for statisticians or biostatisticians, and as a reference for quantitatively minded researchers.
—Statistics in Medicine, 2011
The volume’s editors have assembled a world-class panel of contributors; many have made seminal contributions to the field (this includes the editors themselves). Immediately apparent is the uniformity of notation and writing style not typically found in volumes of this kind. The editors clearly have taken great care to ensure a whole document rather than a disjointed patchwork typical of similar collections. Chapters reflect contributor diversity while suppressing distracting idiosyncrasies. … Experienced researchers and those new to the field will find useful material here. … several chapters provide fresh insights. For graduate students and new researchers, the book provides a useful introduction and comprehensive reference material for the topics it covers. Case studies and software enable readers to implement some methods described in the book, with supplemental datasets and programs appearing on a useful website. … A strong inaugural volume for Chapman & Hall’s new series on modern statistical methods, Longitudinal Data Analysis provides an outstanding model for future entries.
—Biometrics, September 2010
… Longitudinal Data Analysis is the first book to collect and sort through many of the most important developments. The authors make clear the assumptions of the statistical methods and their consequences. Coupled with an abundance of examples, the book guides the practitioner about when to apply one method as opposed to another. The book has remarkable breadth and contains material that would likely be new even to those that analyze longitudinal data on a regular basis. Longitudinal Data Analysis would be useful for applied statisticians looking to expand their analytical toolkit and statistical researchers familiar with the area but looking for a good reference. …an excellent text for a special topics course for Ph.D. students in statistics. It has a good balance of statistical theory and applications, with a large number of real data examples and case studies to illustrate how to use the methods described therein. …a well organized, excellent overview of the state of the art in modeling longitudinal data and would make a useful supplement to the library of anyone that analyzes this type of data.
—Journal of the American Statistical Association, June 2010
…a concise but complete encyclopedia on longitudinal data analysis. The editors have made a great effort to produce a volume providing a comprehensive and up-to-date view of the theory and application of longitudinal data analysis. … One of the strengths of the book is the organizational structure and the fact that the book has been written by well-known experts in the field. … I find this book very useful for statisticians and researchers in many fields where the interest relies on studying the change of an outcome or multiple outcomes over time. Many of the chapters include examples and case studies in different disciplines and some of this material can be found in the website of this book (http://www.biostat.harvard.edu/ fitzmaur/lda). I would like to congratulate the editors and all the contributing authors for preparing this comprehensive handbook on many interesting and complementary aspects of the theory and applications of longitudinal data analysis. This handbook will have, without any doubt, an important place on the shelf of those statisticians and applied researchers working with longitudinal data.
—Journal of Applied Statistics, Vo. 36, No. 10, October 2009
This is public-service broadcasting at its best. Many of the leading internationally recognized experts in the field have been assembled to write a series of expository articles on an important area of modern statistics. … Care has clearly been taken to make the book hang together—it’s not like some ‘edited tomes’ consisting of a set of papers stapled together. There is a mixture of theory and applications with real data, some of which is available on a website. In my opinion the book will be a must-have for anyone seriously involved with repeated measures or longitudinal data.
—International Statistical Review, 2009
Other longitudinal data books do not have the breadth of this one. … I highly recommend this book to anyone interested in learning about modern methods for longitudinal data analysis. I think it would make a particularly good book for a Ph.D.-level reading course or as a supplement to a longitudinal data textbook in a graduate-level course. I especially recommend this book to statistical researchers, as it makes a great reference book.
—Journal of Biopharmaceutical Statistics, Issue 4, 2009